135 lines
4.6 KiB
Markdown
135 lines
4.6 KiB
Markdown
---
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base_model: janhq/Jan-v3-4B-base-instruct
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library_name: gguf
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pipeline_tag: text-generation
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tags:
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- gguf
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- quantized
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- llama-cpp
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---
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# Jan-v3-4B-base-instruct - GGUF
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This is a quantized GGUF version of [janhq/Jan-v3-4B-base-instruct](https://huggingface.co/janhq/Jan-v3-4B-base-instruct) created using [llama.cpp](https://github.com/ggerganov/llama.cpp).
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## Available Quantizations
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| Filename | Quant Type | Description |
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|----------|------------|-------------|
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| Jan-v3-4B-base-instruct.Q2_K.gguf | Q2_K | Smallest, significant quality loss |
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| Jan-v3-4B-base-instruct.Q3_K_S.gguf | Q3_K_S | Very small, low quality |
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| Jan-v3-4B-base-instruct.Q3_K_M.gguf | Q3_K_M | Very small, medium quality |
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| Jan-v3-4B-base-instruct.Q3_K_L.gguf | Q3_K_L | Small, better quality than Q3_K_M |
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| Jan-v3-4B-base-instruct.Q4_0.gguf | Q4_0 | Small, legacy format |
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| Jan-v3-4B-base-instruct.Q4_1.gguf | Q4_1 | Small, legacy format with better accuracy |
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| Jan-v3-4B-base-instruct.Q4_K_S.gguf | Q4_K_S | Small, good quality |
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| Jan-v3-4B-base-instruct.Q4_K_M.gguf | Q4_K_M | Medium, balanced quality - recommended |
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| Jan-v3-4B-base-instruct.Q5_0.gguf | Q5_0 | Medium, legacy format |
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| Jan-v3-4B-base-instruct.Q5_1.gguf | Q5_1 | Medium, legacy format with better accuracy |
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| Jan-v3-4B-base-instruct.Q5_K_S.gguf | Q5_K_S | Medium, good quality |
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| Jan-v3-4B-base-instruct.Q5_K_M.gguf | Q5_K_M | Medium, high quality - recommended |
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| Jan-v3-4B-base-instruct.Q6_K.gguf | Q6_K | Large, very high quality |
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| Jan-v3-4B-base-instruct.Q8_0.gguf | Q8_0 | Large, near-lossless quality |
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## Usage
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### With llama.cpp
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```bash
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./llama-cli -m Jan-v3-4B-base-instruct.Q4_K_M.gguf -p "Your prompt here"
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```
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### With Ollama
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```bash
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ollama run hf.co/aashish1904/Jan-v3-4B-base-instruct-GGUF
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```
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## Original Model
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- **Source**: [janhq/Jan-v3-4B-base-instruct](https://huggingface.co/janhq/Jan-v3-4B-base-instruct)
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- **Quantized by**: GGUF Quantizer Space
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---
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## Original Model Card
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# Jan-v3-4B-base-instruct: a 4B baseline model for fine-tuning
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[](https://github.com/janhq/jan)
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://jan.ai/)
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## Overview
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**Jan-v3-4B-base-instruct** is a 4B-parameter model obtained via post-training distillation from a larger teacher, transferring capabilities while preserving general-purpose performance on standard benchmarks. The result is a compact, ownable base that is straightforward to fine-tune, broadly applicable and minimizing the usual capacity–capability trade-offs.
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Building on this base, **Jan-Code**, a code-tuned variant, **will be released soon.**
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## Model Overview
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This repo contains the BF16 version of **Jan-v3-4B-base-instruct**, which has the following features:
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- Type: Causal Language Models
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- Training Stage: Pretraining & Post-training
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- Number of Parameters: 4B in total
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- Number of Layers: 36
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- Number of Attention Heads (GQA): 32 for Q and 8 for KV
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- Context Length: **262,144 natively**.
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**Intended Use**
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* A better small base for downstream work: improved instruction following out of the box, strong starting point for fine-tuning, and effective lightweight coding assistance.
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## Performance
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## Quick Start
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### Integration with Jan Apps
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Jan-v3 demo is hosted on **Jan Browser** at **[chat.jan.ai](https://chat.jan.ai/)**. It is also optimized for direct integration with [Jan Desktop](https://jan.ai/), select the model in the app to start using it.
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### Local Deployment
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**Using vLLM:**
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```bash
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vllm serve janhq/Jan-v3-4B-base-instruct \
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--host 0.0.0.0 \
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--port 1234 \
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--enable-auto-tool-choice \
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--tool-call-parser hermes
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```
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**Using llama.cpp:**
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```bash
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llama-server --model Jan-v3-4B-base-instruct-Q8_0.gguf \
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--host 0.0.0.0 \
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--port 1234 \
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--jinja \
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--no-context-shift
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```
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### Recommended Parameters
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For optimal performance in agentic and general tasks, we recommend the following inference parameters:
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```yaml
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temperature: 0.7
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top_p: 0.8
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top_k: 20
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```
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## 🤝 Community & Support
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- **Discussions**: [Hugging Face Community](https://huggingface.co/janhq/Jan-v2-VL-8B/discussions)
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- **Jan App**: Learn more about the Jan App at [jan.ai](https://jan.ai/)
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## 📄 Citation
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```bibtex
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Updated Soon
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```
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