4bit Autotrain compatible Base model:finetune:unsloth/ll... Base model:unsloth/llama-3.2-1... Conversational En Endpoints compatible Instruct Llama Quantized Region:us Safetensors Sft Trl Unsloth
Model Card on HF 🤗: https://huggingface.co/dat-lequoc/vLLM-fast-apply-16bit-v0.13-Llama3.2-1B
VLLM Fast Apply 16bit V0.13 Llama3.2 1B Benchmarks
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
What is the LLM Explorer Rank (Score)
VLLM Fast Apply 16bit V0.13 Llama3.2 1B Parameters and Internals
| LLM Name | VLLM Fast Apply 16bit V0.13 Llama3.2 1B |
| Repository 🤗 | https://huggingface.co/dat-lequoc/vLLM-fast-apply-16bit-v0.13-Llama3.2-1B |
| Base Model(s) | |
| Model Size | 1b |
| Required VRAM | 2.5 GB |
| Updated | 2025-07-02 |
| Maintainer | dat-lequoc |
| Model Type | llama |
| Instruction-Based | Yes |
| Model Files | |
| Supported Languages | en |
| Quantization Type | 4bit |
| Model Architecture | LlamaForCausalLM |
| License | apache-2.0 |
| Context Length | 131072 |
| Model Max Length | 131072 |
| Transformers Version | 4.44.2 |
| Tokenizer Class | PreTrainedTokenizerFast |
| Padding Token | <|finetune_right_pad_id|> |
| Vocabulary Size | 128256 |
| Torch Data Type | bfloat16 |
Best Alternatives to VLLM Fast Apply 16bit V0.13 Llama3.2 1B
Best Alternatives |
Context / RAM |
Downloads |
Likes |
|---|---|---|---|
| ...2 1B Instruct Unsloth Bnb 4bit | 128K / 1.1 GB | 213870 | 3 |
| Llama 3.2 1B Instruct Chat Sft | 128K / 2.5 GB | 111 | 0 |
| ...truct Gptqmodel 4bit Vortex V1 | 128K / 1.6 GB | 2049 | 2 |
| ...ama Llama 3.2 1B Instruct FP16 | 128K / 2.5 GB | 1695 | 0 |
| Llama 3.2 1B Instruct 4bit | 128K / 0.7 GB | 22847 | 14 |
| Llama 3.2 1B Instruct Bnb 4bit | 128K / 1 GB | 19248 | 16 |
| Orca Mini V9 5 1B Instruct | 128K / 2.5 GB | 16 | 4 |
| Llama SmolTalk 3.2 1B Instruct | 128K / 2.5 GB | 3003 | 3 |
| Llama Deepsync 1B | 128K / 2.5 GB | 30 | 3 |
| Orca Mini V9 1 1B Instruct | 128K / 2.5 GB | 54 | 3 |
Note: green Score (e.g. "73.2") means that the model is better than dat-lequoc/vLLM-fast-apply-16bit-v0.13-Llama3.2-1B .
Rank the VLLM Fast Apply 16bit V0.13 Llama3.2 1B Capabilities
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124