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README.md CHANGED
@@ -9,69 +9,151 @@ datasets:
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  model-index:
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  - name: starchat2-15b-v0.1
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  results: []
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- quantized_by: bartowski
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- pipeline_tag: text-generation
14
  ---
15
 
16
- ## Exllama v2 Quantizations of starchat2-15b-v0.1
17
 
18
- Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization.
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20
- ## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
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22
- Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
23
 
24
- Conversion was done using the default calibration dataset.
25
 
26
- Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
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28
- Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1
 
 
 
29
 
 
30
 
31
- <a href="https://huggingface.co/bartowski/starchat2-15b-v0.1-exl2/tree/8_0">8.0 bits per weight</a>
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- <a href="https://huggingface.co/bartowski/starchat2-15b-v0.1-exl2/tree/6_5">6.5 bits per weight</a>
 
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35
- <a href="https://huggingface.co/bartowski/starchat2-15b-v0.1-exl2/tree/5_0">5.0 bits per weight</a>
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- <a href="https://huggingface.co/bartowski/starchat2-15b-v0.1-exl2/tree/4_25">4.25 bits per weight</a>
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- <a href="https://huggingface.co/bartowski/starchat2-15b-v0.1-exl2/tree/3_5">3.5 bits per weight</a>
 
 
 
 
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42
- ## Download instructions
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44
- With git:
45
 
46
- ```shell
47
- git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/starchat2-15b-v0.1-exl2
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- ```
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-
50
- With huggingface hub (credit to TheBloke for instructions):
51
-
52
- ```shell
53
- pip3 install huggingface-hub
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- ```
55
 
56
- To download the `main` (only useful if you only care about measurement.json) branch to a folder called `starchat2-15b-v0.1-exl2`:
 
 
57
 
58
- ```shell
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- mkdir starchat2-15b-v0.1-exl2
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- huggingface-cli download bartowski/starchat2-15b-v0.1-exl2 --local-dir starchat2-15b-v0.1-exl2 --local-dir-use-symlinks False
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- ```
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-
63
- To download from a different branch, add the `--revision` parameter:
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65
- Linux:
66
-
67
- ```shell
68
- mkdir starchat2-15b-v0.1-exl2-6_5
69
- huggingface-cli download bartowski/starchat2-15b-v0.1-exl2 --revision 6_5 --local-dir starchat2-15b-v0.1-exl2-6_5 --local-dir-use-symlinks False
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
70
  ```
71
 
72
- Windows (which apparently doesn't like _ in folders sometimes?):
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-
74
- ```shell
75
- mkdir starchat2-15b-v0.1-exl2-6.5
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- huggingface-cli download bartowski/starchat2-15b-v0.1-exl2 --revision 6_5 --local-dir starchat2-15b-v0.1-exl2-6.5 --local-dir-use-symlinks False
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  model-index:
10
  - name: starchat2-15b-v0.1
11
  results: []
 
 
12
  ---
13
 
14
+ <img src="https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/resolve/main/model_logo.png" alt="StarChat2 15B Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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16
+ # Model Card for StarChat2 15B
17
 
18
+ StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat2 is the latest model in the series, and is a fine-tuned version of [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b) that was trained with SFT and DPO on a mix of synthetic datasets.
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20
+ ## Model Details
21
 
22
+ ### Model Description
23
 
24
+ <!-- Provide a longer summary of what this model is. -->
25
 
26
+ - **Model type:** A 16B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
27
+ - **Language(s) (NLP):** Primarily English and 80+ programming languages.
28
+ - **License:** BigCode Open RAIL-M v1
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+ - **Finetuned from model:** [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b)
30
 
31
+ ### Model Sources
32
 
33
+ <!-- Provide the basic links for the model. -->
34
 
35
+ - **Repository:** https://github.com/huggingface/alignment-handbook
36
+ - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground
37
 
38
+ ## Performance
39
 
40
+ StarChat2 15B was trained to balance chat and programming capabilities. It achieves strong performance on chat benchmarks like [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [IFEval](https://arxiv.org/abs/2311.07911), as well as the canonical HumanEval benchmark for Python code completion. The scores reported below were obtained using the [LightEval](https://github.com/huggingface/lighteval) evaluation suite (commit `988959cb905df4baa050f82b4d499d46e8b537f2`) and each prompt has been formatted with the model's corresponding chat template to simulate real-world usage. This is why some scores may differ from those reported in technical reports or on the Open LLM Leaderboard.
41
 
42
+ | Model | MT Bench | IFEval | HumanEval |
43
+ |-------------------------------------------------------------------------------------------------|---------:|-------:|----------:|
44
+ | [starchat2-15b-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1) | 7.66 | 35.12 | 71.34 |
45
+ | [deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) | 4.17 | 14.23 | 80.48 |
46
+ | [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | 6.80 | 43.44 | 50.60 |
47
 
48
 
49
+ ## Intended uses & limitations
50
 
51
+ The model was fine-tuned on a blend of chat, code, math, and reasoning datasets. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground) to test its coding capabilities.
52
 
53
+ Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
 
 
 
 
 
 
 
 
54
 
55
+ ```python
56
+ # pip install 'transformers @ git+https://github.com/huggingface/transformers.git@831bc25d8fdb85768402f772cf65cc3d7872b211'
57
+ # pip install accelerate
58
 
59
+ import torch
60
+ from transformers import pipeline
 
 
 
 
61
 
62
+ pipe = pipeline(
63
+ "text-generation",
64
+ model="HuggingFaceH4/starchat2-15b-v0.1",
65
+ device_map="auto",
66
+ torch_dtype=torch.bfloat16,
67
+ )
68
+ messages = [
69
+ {
70
+ "role": "system",
71
+ "content": "You are StarChat2, an expert programming assistant",
72
+ },
73
+ {"role": "user", "content": "Write a simple website in HTML. When a user clicks the button, it shows a random Chuck Norris joke."},
74
+ ]
75
+ outputs = pipe(
76
+ messages,
77
+ max_new_tokens=512,
78
+ do_sample=True,
79
+ temperature=0.7,
80
+ top_k=50,
81
+ top_p=0.95,
82
+ stop_sequence="<|im_end|>",
83
+ )
84
+ print(outputs[0]["generated_text"][-1]["content"])
85
  ```
86
 
87
+ ## Bias, Risks, and Limitations
88
+
89
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
90
+
91
+ StarChat2 15B has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
92
+ Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the [StarCoder2 dataset](https://huggingface.co/datasets/bigcode/the-stack-v2)
93
+
94
+ Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect.
95
+ For example, it may produce code that does not compile or that produces incorrect results.
96
+ It may also produce code that is vulnerable to security exploits.
97
+ We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking.
98
+
99
+ StarChat2 15B was fine-tuned from the base model [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b), please refer to its model card's [Limitations Section](https://huggingface.co/bigcode/starcoder2-15b#limitations) for relevant information.
100
+ In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its [technical report](https://huggingface.co/papers/2402.19173).
101
+
102
+
103
+ ## Training details
104
+
105
+ This model is a fine-tuned version of [starchat2-15b-sft-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1) on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/orca_dpo_pairs datasets. Check out the recipe in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for more details.
106
+
107
+ It achieves the following results on the evaluation set:
108
+ - Loss: 0.4347
109
+ - Rewards/chosen: -0.9461
110
+ - Rewards/rejected: -2.7745
111
+ - Rewards/accuracies: 0.7658
112
+ - Rewards/margins: 1.8284
113
+ - Logps/rejected: -322.1934
114
+ - Logps/chosen: -316.1898
115
+ - Logits/rejected: -2.3817
116
+ - Logits/chosen: -2.3005
117
+
118
+ ## Training procedure
119
+
120
+ ### Training hyperparameters
121
+
122
+ The following hyperparameters were used during training:
123
+ - learning_rate: 5e-07
124
+ - train_batch_size: 2
125
+ - eval_batch_size: 4
126
+ - seed: 42
127
+ - distributed_type: multi-GPU
128
+ - num_devices: 8
129
+ - gradient_accumulation_steps: 8
130
+ - total_train_batch_size: 128
131
+ - total_eval_batch_size: 32
132
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
133
+ - lr_scheduler_type: cosine
134
+ - lr_scheduler_warmup_ratio: 0.1
135
+ - num_epochs: 2
136
+
137
+ ### Training results
138
+
139
+ | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
140
+ |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
141
+ | 0.717 | 0.17 | 100 | 0.6006 | -0.0924 | -0.2899 | 0.6329 | 0.1975 | -272.5022 | -299.1165 | -2.5313 | -2.4191 |
142
+ | 0.6273 | 0.35 | 200 | 0.5160 | -0.3994 | -0.9461 | 0.6930 | 0.5467 | -285.6261 | -305.2568 | -2.5281 | -2.4278 |
143
+ | 0.5538 | 0.52 | 300 | 0.4781 | -0.6589 | -1.5892 | 0.7247 | 0.9302 | -298.4870 | -310.4470 | -2.4996 | -2.4110 |
144
+ | 0.5056 | 0.7 | 400 | 0.4594 | -0.8283 | -2.1332 | 0.7437 | 1.3050 | -309.3687 | -313.8344 | -2.4472 | -2.3644 |
145
+ | 0.4983 | 0.87 | 500 | 0.4512 | -0.7758 | -2.2806 | 0.7468 | 1.5049 | -312.3167 | -312.7843 | -2.4223 | -2.3404 |
146
+ | 0.4662 | 1.04 | 600 | 0.4431 | -0.7839 | -2.4016 | 0.7658 | 1.6177 | -314.7355 | -312.9465 | -2.4049 | -2.3215 |
147
+ | 0.4411 | 1.22 | 700 | 0.4415 | -1.0090 | -2.7582 | 0.7690 | 1.7492 | -321.8679 | -317.4481 | -2.3840 | -2.3016 |
148
+ | 0.471 | 1.39 | 800 | 0.4368 | -0.9617 | -2.7445 | 0.7690 | 1.7828 | -321.5930 | -316.5019 | -2.3809 | -2.2991 |
149
+ | 0.4485 | 1.57 | 900 | 0.4351 | -0.9490 | -2.7594 | 0.7722 | 1.8103 | -321.8916 | -316.2497 | -2.3815 | -2.3004 |
150
+ | 0.4411 | 1.74 | 1000 | 0.4348 | -0.9293 | -2.7469 | 0.7658 | 1.8176 | -321.6409 | -315.8547 | -2.3823 | -2.3011 |
151
+ | 0.4499 | 1.92 | 1100 | 0.4348 | -0.9482 | -2.7767 | 0.7658 | 1.8285 | -322.2369 | -316.2320 | -2.3828 | -2.3012 |
152
+
153
+
154
+ ### Framework versions
155
+
156
+ - Transformers 4.39.0.dev0
157
+ - Pytorch 2.1.2+cu121
158
+ - Datasets 2.16.1
159
+ - Tokenizers 0.15.1
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+ "train_samples_per_second": 7.445,
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+ "train_steps_per_second": 0.058
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+ }
config.json ADDED
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1
+ {
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+ "_name_or_path": "HuggingFaceH4/starcoder2-15b-ift",
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+ "max_position_embeddings": 16384,
15
+ "mlp_type": "default",
16
+ "model_type": "starcoder2",
17
+ "norm_epsilon": 1e-05,
18
+ "norm_type": "layer_norm",
19
+ "num_attention_heads": 48,
20
+ "num_hidden_layers": 40,
21
+ "num_key_value_heads": 4,
22
+ "residual_dropout": 0.1,
23
+ "rope_theta": 100000,
24
+ "sliding_window": 4096,
25
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26
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27
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29
+ "use_cache": true,
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+ "vocab_size": 49154
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+ }
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+ }
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