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Model: ping98k/open-llama-3b-openthought-mid-4bit
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: openlm-research/open_llama_3b_v2
+tags:
+  - 4bit
+  - bnb
+  - nf4
+  - reasoning
+  - think
+  - openthoughts
+  - sft
+---
+
+# open-llama-3b-openthought-mid-4bit
+
+Merged and 4-bit quantized OpenLLaMA 3B v2 fine-tuned on OpenThoughts-114k reasoning data.
+
+## How it was made
+
+1. Base: [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2)
+2. QLoRA fine-tuned on [open-thoughts/OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k)
+3. Merged LoRA into base (16bit)
+4. Quantized to 4-bit NF4
+
+## Training Data
+
+- open-thoughts/OpenThoughts-114k (DeepSeek-R1 reasoning traces)
+- 10,582 samples after filtering (<= 2024 tokens)
+- 3 epochs, bs=128, cosine LR 2e-4
+- Final loss: 0.71
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("ping98k/open-llama-3b-openthought-mid-4bit", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("ping98k/open-llama-3b-openthought-mid-4bit")
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What is a linked list?"},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
+print(tokenizer.decode(output[0], skip_special_tokens=False))
+```
+
+## LoRA adapter
+
+See [ping98k/open-llama-3b-openthought-mid-lora](https://huggingface.co/ping98k/open-llama-3b-openthought-mid-lora)