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Browse files- LICENSE +133 -0
- README.md +110 -0
- config.json +40 -0
- generation_config.json +12 -0
- model.safetensors.index.json +730 -0
- output-00001-of-00008.safetensors +3 -0
- output-00002-of-00008.safetensors +3 -0
- output-00003-of-00008.safetensors +3 -0
- output-00004-of-00008.safetensors +3 -0
- output-00005-of-00008.safetensors +3 -0
- output-00006-of-00008.safetensors +3 -0
- output-00007-of-00008.safetensors +3 -0
- output-00008-of-00008.safetensors +3 -0
- special_tokens_map.json +31 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2119 -0
LICENSE
ADDED
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@@ -0,0 +1,133 @@
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|
| 1 |
+
META LLAMA 3 COMMUNITY LICENSE AGREEMENT – Adapted For Groq 8B/70B Tool Use
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| 2 |
+
Meta Llama 3 Version Release Date: April 18, 2024
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| 3 |
+
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| 4 |
+
Groq Additional License Terms Release Date: July 17, 2024
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| 5 |
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| 6 |
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“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the
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| 7 |
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Llama Materials set forth herein.
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| 8 |
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| 9 |
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“Documentation” means the specifications, manuals and documentation accompanying Meta Llama 3
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| 10 |
+
distributed by Meta at https://llama.meta.com/get-started/.
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| 11 |
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“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into
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this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or
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regulations to provide legal consent and that has legal authority to bind your employer or such other
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person or entity if you are entering in this Agreement on their behalf.
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“Meta Llama 3” means the foundational large language models and software and algorithms, including
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| 18 |
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machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
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| 19 |
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fine-tuning enabling code and other elements of the foregoing distributed by Meta at
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| 20 |
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https://llama.meta.com/llama-downloads.
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“Llama Materials” means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any
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portion thereof) made available under this Agreement.
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“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your
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principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located
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outside of the EEA or Switzerland).
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By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,
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you agree to be bound by this Agreement.
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1. License Rights and Redistribution.
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a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free
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limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama
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Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the
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Llama Materials.
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b. Redistribution and Use.
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i. If you distribute or make available the Llama Materials (or any derivative works
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thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide
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use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is
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distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model
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iii. You must retain in all copies of the Llama Materials that you distribute the following
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licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights
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iv. Your use of the Llama Materials must comply with applicable laws and regulations
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v. You will not use the Llama Materials or any output or results of the Llama Materials to
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improve any other large language model (excluding Meta Llama 3 or derivative works thereof).
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2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users
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rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
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3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
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OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF
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ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND
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RESULTS.
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LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING
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OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,
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OF THE POSSIBILITY OF ANY OF THE FOREGOING.
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redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to
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use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will
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comply with Meta’s brand guidelines (currently accessible at
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https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use
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of the Mark will inure to the benefit of Meta.
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b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with
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respect to any derivative works and modifications of the Llama Materials that are made by you, as
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between you and Meta, you are and will be the owner of such derivative works and modifications.
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results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other
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rights owned or licensable by you, then any licenses granted to you under this Agreement shall
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terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold
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harmless Meta from and against any claim by any third party arising out of or related to your use or
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distribution of the Llama Materials.
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6. Term and Termination. The term of this Agreement will commence upon your acceptance of this
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Agreement or access to the Llama Materials and will continue in full force and effect until terminated in
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accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in
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breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete
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and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this
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Agreement.
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7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of
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the State of California without regard to choice of law principles, and the UN Convention on Contracts
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for the International Sale of Goods does not apply to this Agreement. The courts of California shall have
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exclusive jurisdiction of any dispute arising out of this Agreement.
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8. Additional terms added by Groq, Inc. specifically for the Groq 8B/70B Tool Use.
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a. The Groq 8B/70B Tool Use is a derivative work that incorporates the MetaLlama3 foundation large language model together with Groq developed material. By using or distributing any portion or element of the Groq 8B/70B Tool Use, you further agree to be bound by both the Meta Community License Agreement in its entirety as stated above for the Licensed Material provided by Meta and this paragraph 8.
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|
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b. The licensed material provided by Groq, Inc. is licensed to you under the Apache License, Version 2.0 (the "License"); you may not use the Groq 8B/70B Tool Use and related material except in compliance with the License. You may obtain a copy of the License at
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| 125 |
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|
| 126 |
+
http://www.apache.org/licenses/LICENSE-2.0
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c. Unless required by applicable law or agreed to in writing, the Groq 8B/70B Tool Use and related material distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
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d. IN NO EVENT WILL GROQ OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF GROQ OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
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| 131 |
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| 132 |
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e. In addition to these additional terms in this Paragraph 8, please note that if you use the Groq 8B/70B Tool Use to create, train, host, fine tune, or otherwise
|
| 133 |
+
improve a third party AI model, which is distributed or made available, you shall also include "Llama 3 Groq" or "llama-3-groq" at the beginning of any such AI model name.
|
README.md
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| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
license: llama3
|
| 5 |
+
pipeline_tag: text-generation
|
| 6 |
+
tags:
|
| 7 |
+
- facebook
|
| 8 |
+
- meta
|
| 9 |
+
- pytorch
|
| 10 |
+
- llama
|
| 11 |
+
- llama-3
|
| 12 |
+
- groq
|
| 13 |
+
- tool-use
|
| 14 |
+
- function-calling
|
| 15 |
+
---
|
| 16 |
+
# BigStorm - ExLLamaV2 (Exl2) Quantization
|
| 17 |
+
- 7.0 bpw target
|
| 18 |
+
- 8 head bits
|
| 19 |
+
|
| 20 |
+
Enjoy! Raise an issue if you'd like other BPW levels.
|
| 21 |
+
**Base Model Card Follows:**
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
# Llama-3-70B-Tool-Use
|
| 25 |
+
|
| 26 |
+
This is the 70B parameter version of the Llama 3 Groq Tool Use model, specifically designed for advanced tool use and function calling tasks.
|
| 27 |
+
|
| 28 |
+
## Model Details
|
| 29 |
+
|
| 30 |
+
- **Model Type:** Causal language model fine-tuned for tool use
|
| 31 |
+
- **Language(s):** English
|
| 32 |
+
- **License:** Meta Llama 3 Community License
|
| 33 |
+
- **Model Architecture:** Optimized transformer
|
| 34 |
+
- **Training Approach:** Full fine-tuning and Direct Preference Optimization (DPO) on Llama 3 70B base model
|
| 35 |
+
- **Input:** Text
|
| 36 |
+
- **Output:** Text, with enhanced capabilities for tool use and function calling
|
| 37 |
+
|
| 38 |
+
## Performance
|
| 39 |
+
|
| 40 |
+
- **Berkeley Function Calling Leaderboard (BFCL) Score:** 90.76% overall accuracy
|
| 41 |
+
- This score represents the best performance among all open-source 70B LLMs on the BFCL
|
| 42 |
+
|
| 43 |
+
## Usage and Limitations
|
| 44 |
+
|
| 45 |
+
This model is designed for research and development in tool use and function calling scenarios. It excels at tasks involving API interactions, structured data manipulation, and complex tool use. However, users should note:
|
| 46 |
+
|
| 47 |
+
- For general knowledge or open-ended tasks, a general-purpose language model may be more suitable
|
| 48 |
+
- The model may still produce inaccurate or biased content in some cases
|
| 49 |
+
- Users are responsible for implementing appropriate safety measures for their specific use case
|
| 50 |
+
|
| 51 |
+
Note the model is quite sensitive to the `temperature` and `top_p` sampling configuration. Start at `temperature=0.5, top_p=0.65` and move up or down as needed.
|
| 52 |
+
|
| 53 |
+
Text prompt example:
|
| 54 |
+
|
| 55 |
+
```
|
| 56 |
+
<|start_header_id|>system<|end_header_id|>
|
| 57 |
+
|
| 58 |
+
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
|
| 59 |
+
<tool_call>
|
| 60 |
+
{"name": <function-name>,"arguments": <args-dict>}
|
| 61 |
+
</tool_call>
|
| 62 |
+
|
| 63 |
+
Here are the available tools:
|
| 64 |
+
<tools> {
|
| 65 |
+
"name": "get_current_weather",
|
| 66 |
+
"description": "Get the current weather in a given location",
|
| 67 |
+
"parameters": {
|
| 68 |
+
"properties": {
|
| 69 |
+
"location": {
|
| 70 |
+
"description": "The city and state, e.g. San Francisco, CA",
|
| 71 |
+
"type": "string"
|
| 72 |
+
},
|
| 73 |
+
"unit": {
|
| 74 |
+
"enum": [
|
| 75 |
+
"celsius",
|
| 76 |
+
"fahrenheit"
|
| 77 |
+
],
|
| 78 |
+
"type": "string"
|
| 79 |
+
}
|
| 80 |
+
},
|
| 81 |
+
"required": [
|
| 82 |
+
"location"
|
| 83 |
+
],
|
| 84 |
+
"type": "object"
|
| 85 |
+
}
|
| 86 |
+
} </tools><|eot_id|><|start_header_id|>user<|end_header_id|>
|
| 87 |
+
|
| 88 |
+
What is the weather like in San Francisco?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
|
| 89 |
+
|
| 90 |
+
<tool_call>
|
| 91 |
+
{"id":"call_deok","name":"get_current_weather","arguments":{"location":"San Francisco","unit":"celsius"}}
|
| 92 |
+
</tool_call><|eot_id|><|start_header_id|>tool<|end_header_id|>
|
| 93 |
+
|
| 94 |
+
<tool_response>
|
| 95 |
+
{"id":"call_deok","result":{"temperature":"72","unit":"celsius"}}
|
| 96 |
+
</tool_response><|eot_id|><|start_header_id|>assistant<|end_header_id|>
|
| 97 |
+
|
| 98 |
+
```
|
| 99 |
+
|
| 100 |
+
## Ethical Considerations
|
| 101 |
+
|
| 102 |
+
While fine-tuned for tool use, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.
|
| 103 |
+
|
| 104 |
+
## Availability
|
| 105 |
+
|
| 106 |
+
The model is available through:
|
| 107 |
+
- [Groq API console](https://console.groq.com)
|
| 108 |
+
- [Hugging Face](https://huggingface.co/Groq/Llama-3-Groq-70B-Tool-Use)
|
| 109 |
+
|
| 110 |
+
For full details on responsible use, ethical considerations, and latest benchmarks, please refer to the [official Llama 3 documentation](https://llama.meta.com/) and the Groq model card.
|
config.json
ADDED
|
@@ -0,0 +1,40 @@
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "../lhopelr",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"LlamaForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
+
"bos_token_id": 128000,
|
| 9 |
+
"eos_token_id": 128009,
|
| 10 |
+
"hidden_act": "silu",
|
| 11 |
+
"hidden_size": 8192,
|
| 12 |
+
"initializer_range": 0.02,
|
| 13 |
+
"intermediate_size": 28672,
|
| 14 |
+
"max_position_embeddings": 8192,
|
| 15 |
+
"mlp_bias": false,
|
| 16 |
+
"model_type": "llama",
|
| 17 |
+
"num_attention_heads": 64,
|
| 18 |
+
"num_hidden_layers": 80,
|
| 19 |
+
"num_key_value_heads": 8,
|
| 20 |
+
"pretraining_tp": 1,
|
| 21 |
+
"rms_norm_eps": 1e-05,
|
| 22 |
+
"rope_scaling": null,
|
| 23 |
+
"rope_theta": 500000.0,
|
| 24 |
+
"tie_word_embeddings": false,
|
| 25 |
+
"torch_dtype": "float16",
|
| 26 |
+
"transformers_version": "4.41.2",
|
| 27 |
+
"use_cache": true,
|
| 28 |
+
"vocab_size": 128262,
|
| 29 |
+
"quantization_config": {
|
| 30 |
+
"quant_method": "exl2",
|
| 31 |
+
"version": "0.1.7",
|
| 32 |
+
"bits": 7.0,
|
| 33 |
+
"head_bits": 8,
|
| 34 |
+
"calibration": {
|
| 35 |
+
"rows": 115,
|
| 36 |
+
"length": 2048,
|
| 37 |
+
"dataset": "(default)"
|
| 38 |
+
}
|
| 39 |
+
}
|
| 40 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 128000,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
128001,
|
| 6 |
+
128009
|
| 7 |
+
],
|
| 8 |
+
"max_length": 4096,
|
| 9 |
+
"temperature": 0.5,
|
| 10 |
+
"top_p": 0.65,
|
| 11 |
+
"transformers_version": "4.41.2"
|
| 12 |
+
}
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,730 @@
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128002": {
|
| 20 |
+
"content": "<|reserved_special_token_0|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128003": {
|
| 28 |
+
"content": "<|reserved_special_token_1|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128004": {
|
| 36 |
+
"content": "<|reserved_special_token_2|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128005": {
|
| 44 |
+
"content": "<|reserved_special_token_3|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128006": {
|
| 52 |
+
"content": "<|start_header_id|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128007": {
|
| 60 |
+
"content": "<|end_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128008": {
|
| 68 |
+
"content": "<|reserved_special_token_4|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128009": {
|
| 76 |
+
"content": "<|eot_id|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128010": {
|
| 84 |
+
"content": "<|reserved_special_token_5|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128011": {
|
| 92 |
+
"content": "<|reserved_special_token_6|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
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| 1745 |
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"content": "<|reserved_special_token_218|>",
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"content": "<|reserved_special_token_221|>",
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"content": "<|reserved_special_token_222|>",
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"content": "<|reserved_special_token_227|>",
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|
| 1868 |
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"content": "<|reserved_special_token_228|>",
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},
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| 1876 |
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"content": "<|reserved_special_token_229|>",
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|
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},
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|
| 1884 |
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"content": "<|reserved_special_token_230|>",
|
| 1885 |
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|
| 1886 |
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|
| 1887 |
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|
| 1888 |
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|
| 1889 |
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"special": true
|
| 1890 |
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},
|
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|
| 1892 |
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"content": "<|reserved_special_token_231|>",
|
| 1893 |
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|
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|
| 1897 |
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},
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|
| 1900 |
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"content": "<|reserved_special_token_232|>",
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},
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"content": "<|reserved_special_token_233|>",
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},
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"content": "<|reserved_special_token_234|>",
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| 1924 |
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"content": "<|reserved_special_token_235|>",
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},
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| 1940 |
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| 1948 |
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"content": "<|reserved_special_token_238|>",
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| 1953 |
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},
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| 1956 |
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"content": "<|reserved_special_token_239|>",
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},
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"content": "<|reserved_special_token_241|>",
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},
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| 1980 |
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"content": "<|reserved_special_token_242|>",
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| 1981 |
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|
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|
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|
| 1984 |
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|
| 1985 |
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},
|
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|
| 1988 |
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"content": "<|reserved_special_token_243|>",
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| 1989 |
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|
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|
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|
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},
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| 2001 |
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},
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| 2004 |
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"content": "<|reserved_special_token_245|>",
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},
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|
| 2012 |
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"content": "<|reserved_special_token_246|>",
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},
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| 2020 |
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"content": "<|reserved_special_token_247|>",
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"content": "<|reserved_special_token_248|>",
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"content": "<|reserved_special_token_249|>",
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},
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"content": "<tool_call>",
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| 2057 |
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| 2058 |
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},
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|
| 2060 |
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"content": "</tool_call>",
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| 2064 |
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| 2065 |
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|
| 2066 |
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},
|
| 2067 |
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"128258": {
|
| 2068 |
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"content": "<tools>",
|
| 2069 |
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|
| 2070 |
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},
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|
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"content": "</tools>",
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},
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"content": "<tool_response>",
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"special": true
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| 2098 |
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}
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| 2099 |
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},
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| 2100 |
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"additional_special_tokens": [
|
| 2101 |
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"<tool_call>",
|
| 2102 |
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"</tool_call>",
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| 2103 |
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"<tools>",
|
| 2104 |
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"</tools>",
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| 2105 |
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"<tool_response>",
|
| 2106 |
+
"</tool_response>"
|
| 2107 |
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],
|
| 2108 |
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"bos_token": "<|begin_of_text|>",
|
| 2109 |
+
"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
|
| 2110 |
+
"clean_up_tokenization_spaces": true,
|
| 2111 |
+
"eos_token": "<|eot_id|>",
|
| 2112 |
+
"model_input_names": [
|
| 2113 |
+
"input_ids",
|
| 2114 |
+
"attention_mask"
|
| 2115 |
+
],
|
| 2116 |
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"model_max_length": 1000000000000000019884624838656,
|
| 2117 |
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"pad_token": "<|eot_id|>",
|
| 2118 |
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"tokenizer_class": "PreTrainedTokenizerFast"
|
| 2119 |
+
}
|