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Model: Mungert/Mistral-7B-Instruct-v0.1-GGUF Source: Original Platform
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---
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license: apache-2.0
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tags:
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- finetuned
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base_model: mistralai/Mistral-7B-v0.1
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pipeline_tag: text-generation
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inference: true
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widget:
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- messages:
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- role: user
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content: What is your favorite condiment?
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extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
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---
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# <span style="color: #7FFF7F;">Mistral-7B-Instruct-v0.1 GGUF Models</span>
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## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>
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Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
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### **Benchmark Context**
|
||||
All tests conducted on **Llama-3-8B-Instruct** using:
|
||||
- Standard perplexity evaluation pipeline
|
||||
- 2048-token context window
|
||||
- Same prompt set across all quantizations
|
||||
|
||||
### **Method**
|
||||
- **Dynamic Precision Allocation**:
|
||||
- First/Last 25% of layers → IQ4_XS (selected layers)
|
||||
- Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
|
||||
- **Critical Component Protection**:
|
||||
- Embeddings/output layers use Q5_K
|
||||
- Reduces error propagation by 38% vs standard 1-2bit
|
||||
|
||||
### **Quantization Performance Comparison (Llama-3-8B)**
|
||||
|
||||
| Quantization | Standard PPL | DynamicGate PPL | Δ PPL | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
|
||||
|--------------|--------------|------------------|---------|----------|---------|--------|-----------|----------|
|
||||
| IQ2_XXS | 11.30 | 9.84 | -12.9% | 2.5G | 2.6G | +0.1G | 234s | 246s |
|
||||
| IQ2_XS | 11.72 | 11.63 | -0.8% | 2.7G | 2.8G | +0.1G | 242s | 246s |
|
||||
| IQ2_S | 14.31 | 9.02 | -36.9% | 2.7G | 2.9G | +0.2G | 238s | 244s |
|
||||
| IQ1_M | 27.46 | 15.41 | -43.9% | 2.2G | 2.5G | +0.3G | 206s | 212s |
|
||||
| IQ1_S | 53.07 | 32.00 | -39.7% | 2.1G | 2.4G | +0.3G | 184s | 209s |
|
||||
|
||||
**Key**:
|
||||
- PPL = Perplexity (lower is better)
|
||||
- Δ PPL = Percentage change from standard to DynamicGate
|
||||
- Speed = Inference time (CPU avx2, 2048 token context)
|
||||
- Size differences reflect mixed quantization overhead
|
||||
|
||||
**Key Improvements:**
|
||||
- 🔥 **IQ1_M** shows massive 43.9% perplexity reduction (27.46 → 15.41)
|
||||
- 🚀 **IQ2_S** cuts perplexity by 36.9% while adding only 0.2GB
|
||||
- ⚡ **IQ1_S** maintains 39.7% better accuracy despite 1-bit quantization
|
||||
|
||||
**Tradeoffs:**
|
||||
- All variants have modest size increases (0.1-0.3GB)
|
||||
- Inference speeds remain comparable (<5% difference)
|
||||
|
||||
|
||||
### **When to Use These Models**
|
||||
📌 **Fitting models into GPU VRAM**
|
||||
|
||||
✔ **Memory-constrained deployments**
|
||||
|
||||
✔ **Cpu and Edge Devices** where 1-2bit errors can be tolerated
|
||||
|
||||
✔ **Research** into ultra-low-bit quantization
|
||||
|
||||
|
||||
## **Choosing the Right Model Format**
|
||||
|
||||
Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.
|
||||
|
||||
### **BF16 (Brain Float 16) – Use if BF16 acceleration is available**
|
||||
- A 16-bit floating-point format designed for **faster computation** while retaining good precision.
|
||||
- Provides **similar dynamic range** as FP32 but with **lower memory usage**.
|
||||
- Recommended if your hardware supports **BF16 acceleration** (check your device's specs).
|
||||
- Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.
|
||||
|
||||
📌 **Use BF16 if:**
|
||||
✔ Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs).
|
||||
✔ You want **higher precision** while saving memory.
|
||||
✔ You plan to **requantize** the model into another format.
|
||||
|
||||
📌 **Avoid BF16 if:**
|
||||
❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower).
|
||||
❌ You need compatibility with older devices that lack BF16 optimization.
|
||||
|
||||
---
|
||||
|
||||
### **F16 (Float 16) – More widely supported than BF16**
|
||||
- A 16-bit floating-point **high precision** but with less of range of values than BF16.
|
||||
- Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs).
|
||||
- Slightly lower numerical precision than BF16 but generally sufficient for inference.
|
||||
|
||||
📌 **Use F16 if:**
|
||||
✔ Your hardware supports **FP16** but **not BF16**.
|
||||
✔ You need a **balance between speed, memory usage, and accuracy**.
|
||||
✔ You are running on a **GPU** or another device optimized for FP16 computations.
|
||||
|
||||
📌 **Avoid F16 if:**
|
||||
❌ Your device lacks **native FP16 support** (it may run slower than expected).
|
||||
❌ You have memory limitations.
|
||||
|
||||
---
|
||||
|
||||
### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference**
|
||||
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
|
||||
- **Lower-bit models (Q4_K)** → **Best for minimal memory usage**, may have lower precision.
|
||||
- **Higher-bit models (Q6_K, Q8_0)** → **Better accuracy**, requires more memory.
|
||||
|
||||
📌 **Use Quantized Models if:**
|
||||
✔ You are running inference on a **CPU** and need an optimized model.
|
||||
✔ Your device has **low VRAM** and cannot load full-precision models.
|
||||
✔ You want to reduce **memory footprint** while keeping reasonable accuracy.
|
||||
|
||||
📌 **Avoid Quantized Models if:**
|
||||
❌ You need **maximum accuracy** (full-precision models are better for this).
|
||||
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).
|
||||
|
||||
---
|
||||
|
||||
### **Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)**
|
||||
These models are optimized for **extreme memory efficiency**, making them ideal for **low-power devices** or **large-scale deployments** where memory is a critical constraint.
|
||||
|
||||
- **IQ3_XS**: Ultra-low-bit quantization (3-bit) with **extreme memory efficiency**.
|
||||
- **Use case**: Best for **ultra-low-memory devices** where even Q4_K is too large.
|
||||
- **Trade-off**: Lower accuracy compared to higher-bit quantizations.
|
||||
|
||||
- **IQ3_S**: Small block size for **maximum memory efficiency**.
|
||||
- **Use case**: Best for **low-memory devices** where **IQ3_XS** is too aggressive.
|
||||
|
||||
- **IQ3_M**: Medium block size for better accuracy than **IQ3_S**.
|
||||
- **Use case**: Suitable for **low-memory devices** where **IQ3_S** is too limiting.
|
||||
|
||||
- **Q4_K**: 4-bit quantization with **block-wise optimization** for better accuracy.
|
||||
- **Use case**: Best for **low-memory devices** where **Q6_K** is too large.
|
||||
|
||||
- **Q4_0**: Pure 4-bit quantization, optimized for **ARM devices**.
|
||||
- **Use case**: Best for **ARM-based devices** or **low-memory environments**.
|
||||
|
||||
---
|
||||
|
||||
### **Summary Table: Model Format Selection**
|
||||
|
||||
| Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
|
||||
|--------------|------------|---------------|----------------------|---------------|
|
||||
| **BF16** | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory |
|
||||
| **F16** | High | High | FP16-supported devices | GPU inference when BF16 isn't available |
|
||||
| **Q4_K** | Medium Low | Low | CPU or Low-VRAM devices | Best for memory-constrained environments |
|
||||
| **Q6_K** | Medium | Moderate | CPU with more memory | Better accuracy while still being quantized |
|
||||
| **Q8_0** | High | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models |
|
||||
| **IQ3_XS** | Very Low | Very Low | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |
|
||||
| **Q4_0** | Low | Low | ARM or low-memory devices | llama.cpp can optimize for ARM devices |
|
||||
|
||||
---
|
||||
|
||||
## **Included Files & Details**
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-bf16.gguf`
|
||||
- Model weights preserved in **BF16**.
|
||||
- Use this if you want to **requantize** the model into a different format.
|
||||
- Best if your device supports **BF16 acceleration**.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-f16.gguf`
|
||||
- Model weights stored in **F16**.
|
||||
- Use if your device supports **FP16**, especially if BF16 is not available.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-bf16-q8_0.gguf`
|
||||
- **Output & embeddings** remain in **BF16**.
|
||||
- All other layers quantized to **Q8_0**.
|
||||
- Use if your device supports **BF16** and you want a quantized version.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-f16-q8_0.gguf`
|
||||
- **Output & embeddings** remain in **F16**.
|
||||
- All other layers quantized to **Q8_0**.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-q4_k.gguf`
|
||||
- **Output & embeddings** quantized to **Q8_0**.
|
||||
- All other layers quantized to **Q4_K**.
|
||||
- Good for **CPU inference** with limited memory.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-q4_k_s.gguf`
|
||||
- Smallest **Q4_K** variant, using less memory at the cost of accuracy.
|
||||
- Best for **very low-memory setups**.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-q6_k.gguf`
|
||||
- **Output & embeddings** quantized to **Q8_0**.
|
||||
- All other layers quantized to **Q6_K** .
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-q8_0.gguf`
|
||||
- Fully **Q8** quantized model for better accuracy.
|
||||
- Requires **more memory** but offers higher precision.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-iq3_xs.gguf`
|
||||
- **IQ3_XS** quantization, optimized for **extreme memory efficiency**.
|
||||
- Best for **ultra-low-memory devices**.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-iq3_m.gguf`
|
||||
- **IQ3_M** quantization, offering a **medium block size** for better accuracy.
|
||||
- Suitable for **low-memory devices**.
|
||||
|
||||
### `Mistral-7B-Instruct-v0.1-q4_0.gguf`
|
||||
- Pure **Q4_0** quantization, optimized for **ARM devices**.
|
||||
- Best for **low-memory environments**.
|
||||
- Prefer IQ4_NL for better accuracy.
|
||||
|
||||
# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
|
||||
❤ **Please click "Like" if you find this useful!**
|
||||
Help me test my **AI-Powered Network Monitor Assistant** with **quantum-ready security checks**:
|
||||
👉 [Quantum Network Monitor](https://readyforquantum.com)
|
||||
|
||||
💬 **How to test**:
|
||||
1. Click the **chat icon** (bottom right on any page)
|
||||
2. Choose an **AI assistant type**:
|
||||
- `TurboLLM` (GPT-4-mini)
|
||||
- `FreeLLM` (Open-source)
|
||||
- `TestLLM` (Experimental CPU-only)
|
||||
|
||||
### **What I’m Testing**
|
||||
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:
|
||||
- **Function calling** against live network services
|
||||
- **How small can a model go** while still handling:
|
||||
- Automated **Nmap scans**
|
||||
- **Quantum-readiness checks**
|
||||
- **Metasploit integration**
|
||||
|
||||
🟡 **TestLLM** – Current experimental model (llama.cpp on 6 CPU threads):
|
||||
- ✅ **Zero-configuration setup**
|
||||
- ⏳ 30s load time (slow inference but **no API costs**)
|
||||
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!
|
||||
|
||||
### **Other Assistants**
|
||||
🟢 **TurboLLM** – Uses **gpt-4-mini** for:
|
||||
- **Real-time network diagnostics**
|
||||
- **Automated penetration testing** (Nmap/Metasploit)
|
||||
- 🔑 Get more tokens by [downloading our Quantum Network Monitor Agent](https://readyforquantum.com/download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)
|
||||
|
||||
🔵 **HugLLM** – Open-source models (≈8B params):
|
||||
- **2x more tokens** than TurboLLM
|
||||
- **AI-powered log analysis**
|
||||
- 🌐 Runs on Hugging Face Inference API
|
||||
|
||||
### 💡 **Example AI Commands to Test**:
|
||||
1. `"Give me info on my websites SSL certificate"`
|
||||
2. `"Check if my server is using quantum safe encyption for communication"`
|
||||
3. `"Run a quick Nmap vulnerability test"`
|
||||
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
|
||||
|
||||
### Final word
|
||||
I fund the servers to create the models files, run the Quantum Network Monitor Service and Pay for Inference from Novita and OpenAI all from my own pocket. All of the code for creating the models and the work I have done with Quantum Network Monitor is [open source](https://github.com/Mungert69). Feel free to use what you find useful. Please support my work and consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) .
|
||||
This will help me pay for the services and increase the token limits for everyone.
|
||||
|
||||
Thank you :)
|
||||
|
||||
|
||||
|
||||
# Model Card for Mistral-7B-Instruct-v0.1
|
||||
|
||||
|
||||
## Encode and Decode with `mistral_common`
|
||||
|
||||
```py
|
||||
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
|
||||
from mistral_common.protocol.instruct.messages import UserMessage
|
||||
from mistral_common.protocol.instruct.request import ChatCompletionRequest
|
||||
|
||||
mistral_models_path = "MISTRAL_MODELS_PATH"
|
||||
|
||||
tokenizer = MistralTokenizer.v1()
|
||||
|
||||
completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
|
||||
|
||||
tokens = tokenizer.encode_chat_completion(completion_request).tokens
|
||||
```
|
||||
|
||||
## Inference with `mistral_inference`
|
||||
|
||||
```py
|
||||
from mistral_inference.transformer import Transformer
|
||||
from mistral_inference.generate import generate
|
||||
|
||||
model = Transformer.from_folder(mistral_models_path)
|
||||
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
|
||||
|
||||
result = tokenizer.decode(out_tokens[0])
|
||||
|
||||
print(result)
|
||||
```
|
||||
|
||||
## Inference with hugging face `transformers`
|
||||
|
||||
```py
|
||||
from transformers import AutoModelForCausalLM
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
|
||||
model.to("cuda")
|
||||
|
||||
generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True)
|
||||
|
||||
# decode with mistral tokenizer
|
||||
result = tokenizer.decode(generated_ids[0].tolist())
|
||||
print(result)
|
||||
```
|
||||
|
||||
> [!TIP]
|
||||
> PRs to correct the `transformers` tokenizer so that it gives 1-to-1 the same results as the `mistral_common` reference implementation are very welcome!
|
||||
|
||||
---
|
||||
|
||||
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
|
||||
|
||||
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
|
||||
|
||||
## Instruction format
|
||||
|
||||
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
|
||||
|
||||
E.g.
|
||||
```
|
||||
text = "<s>[INST] What is your favourite condiment? [/INST]"
|
||||
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
|
||||
"[INST] Do you have mayonnaise recipes? [/INST]"
|
||||
```
|
||||
|
||||
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
device = "cuda" # the device to load the model onto
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
|
||||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "What is your favourite condiment?"},
|
||||
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
|
||||
{"role": "user", "content": "Do you have mayonnaise recipes?"}
|
||||
]
|
||||
|
||||
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
|
||||
|
||||
model_inputs = encodeds.to(device)
|
||||
model.to(device)
|
||||
|
||||
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
|
||||
decoded = tokenizer.batch_decode(generated_ids)
|
||||
print(decoded[0])
|
||||
```
|
||||
|
||||
## Model Architecture
|
||||
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
|
||||
- Grouped-Query Attention
|
||||
- Sliding-Window Attention
|
||||
- Byte-fallback BPE tokenizer
|
||||
|
||||
## Troubleshooting
|
||||
- If you see the following error:
|
||||
```
|
||||
Traceback (most recent call last):
|
||||
File "", line 1, in
|
||||
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
|
||||
config, kwargs = AutoConfig.from_pretrained(
|
||||
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
|
||||
config_class = CONFIG_MAPPING[config_dict["model_type"]]
|
||||
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
|
||||
raise KeyError(key)
|
||||
KeyError: 'mistral'
|
||||
```
|
||||
|
||||
Installing transformers from source should solve the issue
|
||||
pip install git+https://github.com/huggingface/transformers
|
||||
|
||||
This should not be required after transformers-v4.33.4.
|
||||
|
||||
## Limitations
|
||||
|
||||
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
|
||||
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
|
||||
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
|
||||
|
||||
## The Mistral AI Team
|
||||
|
||||
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
Reference in New Issue
Block a user