--- language: - en license: apache-2.0 tags: - text-generation - TensorBlock - GGUF base_model: Felladrin/Smol-Llama-101M-Chat-v1 datasets: - Open-Orca/SlimOrca-Dedup - VMware/open-instruct - LDJnr/Capybara - cognitivecomputations/ultrachat-uncensored - starfishmedical/webGPT_x_dolly - THUDM/webglm-qa widget: - messages: - role: system content: You are a helpful assistant who gives creative responses. - role: user content: Write the background story of a game about wizards and llamas in a sci-fi world. - messages: - role: system content: A friendly chat between a user and an assistant. - role: user content: Got a question for you! - role: assistant content: Sure! What's it? - role: user content: I need to build a simple website. Where should I start learning about web development? - messages: - role: system content: You are a helpful assistant who provides concise answers to the user's questions. - role: user content: How to become more healthy? - messages: - role: system content: You are a helpful assistant, who always answers with empathy. - role: user content: List the pros and cons of social media. - messages: - role: system content: You are a helpful assistant, who always answers with empathy. - role: user content: Hello! - role: assistant content: Hi! How can I help you today? - role: user content: 'Take a look at the info below. - The tape inside the VHS cassettes is very delicate and can be easily ruined, making them unplayable and unrepairable. The reason the tape deteriorates is that the magnetic charge needed for them to work is not permanent, and the magnetic particles end up losing their charge in a process known as remanence decay. These particles could also become demagnetised via being stored too close to a magnetic source. - One of the most significant issues with VHS tapes is that they have moving parts, meaning that there are more occasions when something can go wrong, damaging your footage or preventing it from playing back. The tape itself is a prominent cause of this, and tape slippage can occur. Tapes slippage can be caused when the tape loses its tension, or it has become warped. These problems can occur in storage due to high temperatures or frequent changes in humidity. - VHS tapes deteriorate over time from infrequent or overuse. Neglect means mold and dirt, while overuse can lead to scratches and technical difficulties. This is why old VHS tapes inevitably experience malfunctions after a long period of time. Usually anywhere between 10 to 25+ years. - Some VHS tapes like newer mini DVs and Digital 8 tapes can suffer from digital corruption, meaning that the footage becomes lost and cannot be recovered. These tapes were the steppingstone from VHS to the digital age when capturing footage straight to digital became the norm. Unfortunately,they are susceptible to digital corruption, which causes video pixilation and/or loss of audio.' - role: assistant content: Alright! - role: user content: 'Now I''m going to write my question, and if the info above is useful, you can use them in your response. Ready?' - role: assistant content: Ready for your question! - role: user content: Why do VHS tapes deteriorate over time? inference: parameters: max_new_tokens: 250 penalty_alpha: 0.5 top_k: 4 repetition_penalty: 1.105 model-index: - name: Smol-Llama-101M-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: 22.87 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Smol-Llama-101M-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: 28.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Smol-Llama-101M-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: 24.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Smol-Llama-101M-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: 45.76 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Smol-Llama-101M-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: 50.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Smol-Llama-101M-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: 0.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Smol-Llama-101M-Chat-v1 name: Open LLM Leaderboard ---
TensorBlock

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

## Felladrin/Smol-Llama-101M-Chat-v1 - GGUF This repo contains GGUF format model files for [Felladrin/Smol-Llama-101M-Chat-v1](https://huggingface.co/Felladrin/Smol-Llama-101M-Chat-v1). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](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 | | -------- | ---------- | --------- | ----------- | | [Smol-Llama-101M-Chat-v1-Q2_K.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q2_K.gguf) | Q2_K | 0.048 GB | smallest, significant quality loss - not recommended for most purposes | | [Smol-Llama-101M-Chat-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q3_K_S.gguf) | Q3_K_S | 0.054 GB | very small, high quality loss | | [Smol-Llama-101M-Chat-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q3_K_M.gguf) | Q3_K_M | 0.056 GB | very small, high quality loss | | [Smol-Llama-101M-Chat-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q3_K_L.gguf) | Q3_K_L | 0.059 GB | small, substantial quality loss | | [Smol-Llama-101M-Chat-v1-Q4_0.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q4_0.gguf) | Q4_0 | 0.064 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Smol-Llama-101M-Chat-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q4_K_S.gguf) | Q4_K_S | 0.064 GB | small, greater quality loss | | [Smol-Llama-101M-Chat-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q4_K_M.gguf) | Q4_K_M | 0.065 GB | medium, balanced quality - recommended | | [Smol-Llama-101M-Chat-v1-Q5_0.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q5_0.gguf) | Q5_0 | 0.074 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Smol-Llama-101M-Chat-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q5_K_S.gguf) | Q5_K_S | 0.074 GB | large, low quality loss - recommended | | [Smol-Llama-101M-Chat-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q5_K_M.gguf) | Q5_K_M | 0.074 GB | large, very low quality loss - recommended | | [Smol-Llama-101M-Chat-v1-Q6_K.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q6_K.gguf) | Q6_K | 0.084 GB | very large, extremely low quality loss | | [Smol-Llama-101M-Chat-v1-Q8_0.gguf](https://huggingface.co/tensorblock/Smol-Llama-101M-Chat-v1-GGUF/blob/main/Smol-Llama-101M-Chat-v1-Q8_0.gguf) | Q8_0 | 0.108 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/Smol-Llama-101M-Chat-v1-GGUF --include "Smol-Llama-101M-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/Smol-Llama-101M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```