---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---


# 🚀 OpenClaw Continuous Pretraining Model (README.md)
👉 **Try it instantly on Colab:**  
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1BwrFHtGHNQl5hXp8AHI2SJbiwEK3qwQM?usp=sharing)
---

## 💡 Ask anything about OpenClaw

This model is continuously pretrained on **OpenClaw `.md` files**, making it highly specialized for understanding, explaining, and helping you work with the OpenClaw ecosystem.

You can ask things like:

* How to set up OpenClaw
* How to use OpenClaw with Docker
* Debugging issues
* Understanding configs, workflows, and usage

---

## 🧠 Model Details

* **Base Model:** Mistral 7B
* **Training Type:** Continuous Pretraining (LoRA Adapter)
* **Dataset:** OpenClaw Markdown files (`.md`)
* **Framework:** Unsloth + Hugging Face Transformers
* **Optimization:** 4-bit quantization support

---

## ⚡ Quick Start (Inference Code)

```python
from unsloth import FastLanguageModel
import torch

max_seq_length = 2048  # Supports RoPE scaling internally
dtype = None           # Auto detect (Float16 / BFloat16)
load_in_4bit = True    # Reduce memory usage

from transformers import TextStreamer

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/mistral-7b-v0.3",
    max_seq_length=2048,
)

# Load OpenClaw adapter
model.load_adapter("Ishant06/OpenClaw-Continuous-Pretraining")

# Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"

# ---- TEST INPUT ----
prompt = "how to use openclaw with docker?"

inputs = tokenizer(
    prompt,
    return_tensors="pt"
).to(device)

# Generate output
outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)

# Decode response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("\n=== RESPONSE ===\n")
print(response)
```

---

## 🔥 Features

* 📚 Trained on real OpenClaw documentation
* ⚡ Fast inference using Unsloth
* 🧠 Better understanding of structured `.md` data
* 💻 Efficient on low VRAM (4-bit quantization)

---

## 🛠️ Use Cases

* OpenClaw documentation assistant
* Developer Q&A bot
* Debugging and setup guidance
* Learning OpenClaw faster

---

## 📌 Notes

* This is a **LoRA adapter**, not a full standalone model
* Requires base model: `unsloth/mistral-7b-v0.3`
* Best suited for OpenClaw-related queries

---

## ⭐ Support

If you find this useful:

* ⭐ Star the repo
* 🤝 Share with others
* 🛠️ Contribute improvements


# Uploaded  model

- **Developed by:** Ishant06
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit

This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)