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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation |
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- TensorBlock |
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- GGUF |
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datasets: |
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- THUDM/webglm-qa |
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- databricks/databricks-dolly-15k |
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- cognitivecomputations/wizard_vicuna_70k_unfiltered |
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- totally-not-an-llm/EverythingLM-data-V3 |
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- Amod/mental_health_counseling_conversations |
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- sablo/oasst2_curated |
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- starfishmedical/webGPT_x_dolly |
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- Open-Orca/OpenOrca |
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- mlabonne/chatml_dpo_pairs |
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base_model: Felladrin/Llama-68M-Chat-v1 |
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widget: |
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- messages: |
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- role: system |
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content: You are a career counselor. The user will provide you with an individual |
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looking for guidance in their professional life, and your task is to assist |
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them in determining what careers they are most suited for based on their skills, |
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interests, and experience. You should also conduct research into the various |
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options available, explain the job market trends in different industries, and |
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advice on which qualifications would be beneficial for pursuing particular fields. |
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- role: user |
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content: Heya! |
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- role: assistant |
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content: Hi! How may I help you? |
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- role: user |
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content: I am interested in developing a career in software engineering. What |
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would you recommend me to do? |
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- messages: |
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- role: system |
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content: You are a knowledgeable assistant. Help the user as much as you can. |
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- role: user |
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content: How to become healthier? |
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- messages: |
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- role: system |
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content: You are a helpful assistant who provides concise responses. |
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- role: user |
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content: Hi! |
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- role: assistant |
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content: Hello there! How may I help you? |
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- role: user |
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content: I need to build a simple website. Where should I start learning about |
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web development? |
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- messages: |
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- role: system |
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content: You are a very creative assistant. User will give you a task, which you |
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should complete with all your knowledge. |
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- role: user |
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content: Write the background story of an RPG game about wizards and dragons in |
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a sci-fi world. |
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inference: |
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parameters: |
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max_new_tokens: 64 |
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penalty_alpha: 0.5 |
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top_k: 4 |
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model-index: |
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- name: Llama-68M-Chat-v1 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 23.29 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 28.27 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 25.18 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 47.27 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 54.3 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 0.0 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 |
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name: Open LLM Leaderboard |
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--- |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"> |
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> |
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</p> |
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</div> |
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</div> |
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## Felladrin/Llama-68M-Chat-v1 - GGUF |
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This repo contains GGUF format model files for [Felladrin/Llama-68M-Chat-v1](https://huggingface.co/Felladrin/Llama-68M-Chat-v1). |
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). |
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<div style="text-align: left; margin: 20px 0;"> |
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> |
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Run them on the TensorBlock client using your local machine ↗ |
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</a> |
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</div> |
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## Prompt template |
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``` |
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<|im_start|>system |
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{system_prompt}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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``` |
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## Model file specification |
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| Filename | Quant type | File Size | Description | |
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| -------- | ---------- | --------- | ----------- | |
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| [Llama-68M-Chat-v1-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q2_K.gguf) | Q2_K | 0.033 GB | smallest, significant quality loss - not recommended for most purposes | |
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| [Llama-68M-Chat-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q3_K_S.gguf) | Q3_K_S | 0.037 GB | very small, high quality loss | |
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| [Llama-68M-Chat-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q3_K_M.gguf) | Q3_K_M | 0.038 GB | very small, high quality loss | |
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| [Llama-68M-Chat-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q3_K_L.gguf) | Q3_K_L | 0.039 GB | small, substantial quality loss | |
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| [Llama-68M-Chat-v1-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q4_0.gguf) | Q4_0 | 0.042 GB | legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [Llama-68M-Chat-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q4_K_S.gguf) | Q4_K_S | 0.042 GB | small, greater quality loss | |
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| [Llama-68M-Chat-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q4_K_M.gguf) | Q4_K_M | 0.043 GB | medium, balanced quality - recommended | |
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| [Llama-68M-Chat-v1-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q5_0.gguf) | Q5_0 | 0.047 GB | legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [Llama-68M-Chat-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q5_K_S.gguf) | Q5_K_S | 0.047 GB | large, low quality loss - recommended | |
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| [Llama-68M-Chat-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q5_K_M.gguf) | Q5_K_M | 0.048 GB | large, very low quality loss - recommended | |
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| [Llama-68M-Chat-v1-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q6_K.gguf) | Q6_K | 0.053 GB | very large, extremely low quality loss | |
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| [Llama-68M-Chat-v1-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q8_0.gguf) | Q8_0 | 0.068 GB | very large, extremely low quality loss - not recommended | |
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## Downloading instruction |
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### Command line |
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Firstly, install Huggingface Client |
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```shell |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, downoad the individual model file the a local directory |
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```shell |
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huggingface-cli download tensorblock/Llama-68M-Chat-v1-GGUF --include "Llama-68M-Chat-v1-Q2_K.gguf" --local-dir MY_LOCAL_DIR |
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``` |
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: |
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```shell |
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huggingface-cli download tensorblock/Llama-68M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' |
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``` |
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