GGUF Q5_K_M mistralai_Mistral-7B-Instruct-v0.3-F16.gguf-Q5_K_M

This is a Q5_K_M GGUF quantized variant of mistralai/Mistral-7B-Instruct-v0.3, optimized for fast inference using llama.cpp in memory-constrained environments.

Overview

This model is a Q5_K_M quantized GGUF version of Mistral-7B-Instruct-v0.3, optimized for fast inference on CPU/GPU using llama.cpp. It is instruction-tuned and supports v3 tokenizer format and function calling, making it suitable for conversational agents and instruct-following tasks in low-latency deployment environments.

Quantization Details

This model was quantized using the llama-quantize binary from the llama-cpp-python project, which wraps llama.cpp's quantization framework. The Q5_K_M format strikes a balance between latency, model size, and output quality, offering competitive performance with minimal degradation in instruction-following ability.

Fidelity Evaluation

Quantized outputs were evaluated against the original full-precision checkpoint using a suite of standard text similarity metrics:

-ROUGE-L F1

-BLEU

-Cosine Similarity (CLS embeddings)

-BERTScore F1

These scores help verify that the quantized model retains semantic and structural fidelity to the original, making it a strong candidate for downstream applications in memory-constrained settings.

Model Architecture

Attribute	Value
Model class	MistralForCausalLM
Number of parameters	12,200,000,000
Hidden size	4096
Number of layers	32
Attention heads	32
Vocabulary size	32000
Compute dtype	float16 (quantized via llama.cpp)

Quantization Configuration

The following configuration dictionary was used during quantization:

Quant type: "Q5_K_M", base GGUF format from llama.cpp

Intended Use

Research and experimentation.
Instruction-following tasks in resource-constrained environments.
Demonstrations of quantized model capabilities.

Limitations

May reproduce biases from the original model.
Quantization may reduce generation diversity and factual accuracy.
Not intended for production without additional evaluation.

Usage

./llama-cli -m mistralai_Mistral-7B-Instruct-v0.3-F16.gguf-Q5_K_M.gguf -p "Explain the concept of reinforcement learning."

Generate response from the quantized model using chat-style inference

llm = Llama(
    model_path=str(quant_output_path),
    n_ctx=2048,
    n_gpu_layers=40
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": prompt}],
    max_tokens=1024
)
quantized_output = output['choices'][0]['message']['content']

Prompt Used for Evaluation

Explain the concept of reinforcement learning, including an example of how it works in real-world applications. Please format the response in a clear and concise manner suitable for someone with a basic understanding of machine learning.

Reference Output (Full-weight Model)

Title: Reinforcement Learning: A Simple Explanation and Real-World Application  Reinforcement Learning (RL) is a type of Machine Learning (ML) where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent is rewarded or penalized (with positive or negative rewards, respectively) for its actions, with the goal being to learn a series of actions that maximize the total reward.  Here's a simple breakdown of the process:  Initial State: The agent starts in an initial state (e.g., a random position in a game).  Action Selection: The agent selects an action based on its current policy (a strategy for choosing actions). Initially, the policy is random, but it improves over time.  State Transition: The environment transitions to a new state based on the action taken.  Reward: The environment provides a reward or penalty for the action taken. The reward signal guides the learning process.  Update Policy: The agent updates its policy based on the experience gained from the action and its reward. This is typically done using techniques like Q-Learning or Deep Q-Networks (DQN).  Repeat: The agent repeats the process, aiming to learn a policy that maximizes the total reward over time.  A real-world example of reinforcement learning is the Google DeepMind's AlphaGo, which learned to play the complex board game Go at a superhuman level. AlphaGo used a type of RL called Monte Carlo Tree Search (MCTS) combined with a neural network to select moves. It learned by playing against itself, with the reward being a win or loss. Over time, AlphaGo improved its play by learning from its mistakes and successes.  In summary, Reinforcement Learning is a powerful ML technique that allows agents to learn optimal behavior in complex, dynamic environments. It's particularly useful in situations where the optimal action is not explicitly defined, but can be learned through trial and error.

Quantized Model Output

 Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The goal is to maximize a reward signal over time. Here's a simplified breakdown:

1. **Agent**: The learning entity that interacts with the environment.

2. **Environment**: The world in which the agent acts. It can be a physical or virtual system.

3. **Actions**: The choices the agent can make to influence the environment.

4. **Rewards**: Feedback the environment provides to the agent for its actions. The goal is to learn a policy that maximizes the total reward over time.

The learning process in RL is based on trial and error. The agent takes an action, observes the resulting state of the environment, and receives a reward. It then updates its understanding of the best action to take in a given state, with the aim of maximizing the long-term reward.

A real-world example of reinforcement learning is Google's DeepMind's AlphaGo, which learned to play the complex board game Go at a superhuman level. AlphaGo used RL to learn the optimal moves by playing against itself, learning from its mistakes, and improving its strategy over time.

In the game, the environment is the Go board, the agent is AlphaGo, and the actions are the possible moves it can make. The reward could be a positive score for winning the game and a negative score for losing. AlphaGo learned to evaluate the potential outcomes of each move, allowing it to make strategic decisions and ultimately master the game.

In summary, reinforcement learning is a powerful technique for training agents to make optimal decisions in complex and dynamic environments. It's particularly useful in scenarios where the optimal policy is not easily defined, and the agent must learn through interaction and feedback.

Evaluation Metrics

Metric	Value
ROUGE-L F1	0.4283
BLEU	0.2378
Cosine Similarity	0.9399
BERTScore F1	0.4589

Higher ROUGE and BLEU scores indicate closer alignment with the original output.

Interpretation: The quantized model output exhibits moderate similarity to the full-weight model.

Warning: The quantized output has 22 sentences, while the reference has 19. This may indicate structural divergence.

Model Files Metadata

Filename	Size (bytes)	SHA-256
`mistralai_Mistral-7B-Instruct-v0.3-F16.gguf-Q5_K_M.gguf`	5136179200	`3230f313bd79235184ccbd7430e5328a10a11f6d2a4a3c6f1d03b62edebc6eb6`
`mistralai_Mistral-7B-Instruct-v0.3-F16.gguf`	14497341440	`1646f811e7a56f85f2cb60d2a55529482ce090c23bc47acdffdd56bcb1226181`

Notes

Produced on 2025-07-22T19:05:54.018350.
Quantized automatically using llama-quantize.

Intended primarily for research and experimentation.

Citation

Mistralai/Mistral-7B-Instruct-v0.3

Mistral 7B Announcement

License

This model is distributed under the Apache 2.0 license, consistent with the original Mistral-7B-Instruct-v0.3.

Model Card Authors

This quantized model was prepared by PJEDeveloper.