Akshaykumarbm
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OpenAssisted-English-Mistral-7b

@@ -1,199 +1,197 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- mistral-8b
+- openassistant
+- openassisted-english
+- language-modeling
+- text-generation
+- conversational-ai
+license: apache-2.0
+language:
+- en
+base_model:
+- mistralai/Mistral-7B-Instruct-v0.1
 ---
+# Mistral-8B Instruction-Tuned on OpenAssisted-English
+This model is a fine-tuned version of [Mistral-8B](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [OpenAssisted-English](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset using Hugging Face's `transformers` library. The model is optimized for high-quality conversational and instruction-following tasks in English.
+---
 ## Model Details
 ### Model Description
+This model is an instruction-tuned version of the Mistral-8B architecture, fine-tuned specifically to follow human instructions and engage in helpful, safe, and factual conversations. It leverages the OpenAssisted-English dataset, a cleaned and filtered subset from OpenAssistant's OASST1 dataset.
+* **Developed by:** Akshay Kumar BM
+* **Fine-tuned using:** Hugging Face Transformers
+* **Dataset used:** OpenAssisted-English (from OpenAssistant)
+* **Model type:** Decoder-only Transformer
+* **Language(s):** English
+* **License:** Apache 2.0
+* **Finetuned from model:** mistralai/Mistral-7B-v0.1
+---
+## Model Sources
+* **Base Model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+* **Dataset:** [OpenAssisted-English](https://huggingface.co/datasets/OpenAssistant/oasst1)
+* **Library:** Hugging Face Transformers
+* **Frameworks:** PyTorch, Accelerate
+---
 ## Uses
 ### Direct Use
+* Conversational AI
+* Instruction-following agents
+* Text completion and generation
+* Chatbot backends
+* Question answering
+### Downstream Use
+* Fine-tuning for specific domains (e.g., legal, medical, education)
+* Integration into multi-agent systems or RAG pipelines
+* Prompt engineering and prototyping
 ### Out-of-Scope Use
+* Use in high-risk environments (e.g., medical diagnosis, legal decision making) without human oversight.
+* Generating misinformation, harmful, offensive, or biased content.
+* Any use violating Hugging Face’s or Apache 2.0 licensing terms.
+---
 ## Bias, Risks, and Limitations
+Despite being fine-tuned for alignment, the model may:
+* Hallucinate facts.
+* Reflect biases present in the OpenAssistant dataset.
+* Respond unpredictably to adversarial or ambiguous prompts.
 ### Recommendations
+* Always include a human-in-the-loop for sensitive applications.
+* Evaluate in domain-specific scenarios before deployment.
+* Apply additional safety filters for production use.
+---
+## How to Get Started
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "Akshaykumarbm/OpenAssisted-English-Mistral-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+input_prompt = "Explain quantum computing in simple terms."
+inputs = tokenizer(input_prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+---
 ## Training Details
 ### Training Data
+The model was trained on the **OpenAssisted-English** dataset, which includes high-quality, human-annotated instruction-response pairs derived from OpenAssistant’s OASST1 dataset.
+* Format: Instruction + Response
+* Filters: Language = English, Quality ≥ 3, Assistant messages only
+* Size: \~100k samples
 ### Training Procedure
+#### Preprocessing
+* Tokenization: BPE tokenizer from Mistral
+* Truncation: 4096 tokens
+* Format: `<s>[INST] prompt [/INST] response</s>`
+#### Hyperparameters
+* **Precision:** bf16 mixed precision
+* **Batch size:** 512 (global)
+* **Epochs:** 15
+* **Optimizer:** AdamW
+* **LR Scheduler:** CosineDecay
+* **Learning rate:** 2e-5
+* **Warmup steps:** 500
+#### Compute
+* **Hardware:** AMD MI300
+* **Training time:** \~18 hours
+* **Frameworks:** PyTorch + Accelerate + DDP
+---
 ## Evaluation
+### Testing Data
+* Held-out subset from OpenAssisted-English
+* Manual eval for coherence, helpfulness, and safety
+* Evaluation on MT-Bench and AlpacaEval (optional)
+### Metrics
+* **Helpfulness Score** (manual): \~7.2/10
+* **Toxicity (Perspective API):** <1%
+* **BLEU, ROUGE:** Used to compare with gold responses
+---
+## Technical Specifications
+* **Architecture:** Mistral 8B (decoder-only transformer)
+* **Tokenizer:** Mistral Tokenizer (32k vocab)
+* **Context Length:** 8k tokens
+* **Parameters:** \~8.1 billion
+---
+## Citation
+If you use this model, please cite the original Mistral model and OpenAssistant dataset.
+```bibtex
+@misc{mistral2023,
+  title={Mistral 7B},
+  author={Mistral AI},
+  year={2023},
+  url={https://mistral.ai/news/announcing-mistral-7b/}
+}
+@misc{openassistant2023,
+  title = {OpenAssistant Conversations - OASST1},
+  author = {OpenAssistant Contributors},
+  year = {2023},
+  url = {https://huggingface.co/datasets/OpenAssistant/oasst1}
+}
+```
+---
+## Contact
+* **Author:** Akshay Kumar BM
+* **Email:** [akshaykumarbedre.bm@gmail.com](mailto:akshaykumarbedre.bm@gmail.com)
+* **GitHub:** [akshaykumarbedre](https://github.com/akshaykumarbedre)
+* **Hugging Face:** [akshaykumarbm](https://huggingface.co/akshaykumarbm)
+---