--- language: - en license: apache-2.0 base_model: Felladrin/Pythia-31M-Chat-v1 datasets: - totally-not-an-llm/EverythingLM-data-V3 - databricks/databricks-dolly-15k - THUDM/webglm-qa - starfishmedical/webGPT_x_dolly - Amod/mental_health_counseling_conversations - sablo/oasst2_curated - cognitivecomputations/wizard_vicuna_70k_unfiltered - mlabonne/chatml_dpo_pairs pipeline_tag: text-generation widget: - messages: - role: system content: You are a career counselor. The user will provide you with an individual looking for guidance in their professional life, and your task is to assist them in determining what careers they are most suited for based on their skills, interests, and experience. You should also conduct research into the various options available, explain the job market trends in different industries, and advice on which qualifications would be beneficial for pursuing particular fields. - role: user content: Heya! - role: assistant content: Hi! How may I help you? - role: user content: I am interested in developing a career in software engineering. What would you recommend me to do? - messages: - role: system content: You are a helpful assistant who answers user's questions with details and curiosity. - role: user content: What are some potential applications for quantum computing? - messages: - role: system content: You are a highly knowledgeable assistant. Help the user as much as you can. - role: user content: What are some steps I can take to become a healthier person? inference: parameters: max_new_tokens: 250 penalty_alpha: 0.5 top_k: 2 repetition_penalty: 1.0016 tags: - TensorBlock - GGUF model-index: - name: Pythia-31M-Chat-v1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 22.7 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 25.6 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 23.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 47.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 0.0 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard ---
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## Felladrin/Pythia-31M-Chat-v1 - GGUF This repo contains GGUF format model files for [Felladrin/Pythia-31M-Chat-v1](https://huggingface.co/Felladrin/Pythia-31M-Chat-v1). 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).
Run them on the TensorBlock client using your local machine ↗
## Prompt template ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Pythia-31M-Chat-v1-Q2_K.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q2_K.gguf) | Q2_K | 0.017 GB | smallest, significant quality loss - not recommended for most purposes | | [Pythia-31M-Chat-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q3_K_S.gguf) | Q3_K_S | 0.019 GB | very small, high quality loss | | [Pythia-31M-Chat-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q3_K_M.gguf) | Q3_K_M | 0.019 GB | very small, high quality loss | | [Pythia-31M-Chat-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q3_K_L.gguf) | Q3_K_L | 0.019 GB | small, substantial quality loss | | [Pythia-31M-Chat-v1-Q4_0.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q4_0.gguf) | Q4_0 | 0.021 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Pythia-31M-Chat-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q4_K_S.gguf) | Q4_K_S | 0.021 GB | small, greater quality loss | | [Pythia-31M-Chat-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q4_K_M.gguf) | Q4_K_M | 0.021 GB | medium, balanced quality - recommended | | [Pythia-31M-Chat-v1-Q5_0.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q5_0.gguf) | Q5_0 | 0.023 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Pythia-31M-Chat-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q5_K_S.gguf) | Q5_K_S | 0.023 GB | large, low quality loss - recommended | | [Pythia-31M-Chat-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q5_K_M.gguf) | Q5_K_M | 0.023 GB | large, very low quality loss - recommended | | [Pythia-31M-Chat-v1-Q6_K.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q6_K.gguf) | Q6_K | 0.025 GB | very large, extremely low quality loss | | [Pythia-31M-Chat-v1-Q8_0.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q8_0.gguf) | Q8_0 | 0.032 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Pythia-31M-Chat-v1-GGUF --include "Pythia-31M-Chat-v1-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Pythia-31M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```