This model is a Q5_K_M quantized GGUF version of Mistral-7B-Instruct-v0.2, optimized for fast inference on CPU/GPU using llama.cpp. The Mistral-7B-Instruct-v0.2 model is an instruction-tuned version of Mistral-7B-v0.2 designed for conversational and instruction-following tasks.
Key improvements in v0.2 over v0.1 include:
32k context window (previously 8k)
RoPE-theta = 1e6
Removed Sliding-Window Attention
This quantized model uses the GGUF format and is optimized for low-latency inference on CPUs and GPUs, making it suitable for resource-constrained environments and edge deployment.
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.
This model was quantized using the llama-quantize tool from the llama-cpp-python project, which is built on top of the llama.cpp backend.
The Q5_K_M quantization format offers an excellent balance of model size, inference speed, and quality, preserving strong instruction-following performance with minimal degradation.
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.
No moderation layer included; outputs may require external filtering.
Usage
./llama-cli -m mistralai_Mistral-7B-Instruct-v0.2-Q5_K_M.gguf -p "Explain the concept of reinforcement learning."
Generate response with llama-cpp-python from the quantized model using chat-style inference
1from transformers import AutoModelForCausalLM, AutoTokenizer
23device ="cuda"4model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2").to(device)5tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")67messages =[8{"role":"user","content":"What is your favourite condiment?"},9{"role":"assistant","content":"Well, I'm quite partial to a good squeeze of fresh lemon juice..."},10{"role":"user","content":"Do you have mayonnaise recipes?"}11]121314inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)15generated_ids = model.generate(inputs, max_new_tokens=1000, do_sample=True)16print(tokenizer.decode(generated_ids[0]))
Instruction Format
To leverage the instruction fine-tuning, prompts should follow the [INST] ... [/INST] format:
Example:
python
1<s>[INST] What is your favourite condiment? [/INST]2Well, I'm quite partial to a good squeeze of fresh lemon juice...</s>3[INST] Do you have mayonnaise recipes? [/INST]
This is also supported via the Hugging Face tokenizer's apply_chat_template() method.
Prompt Used for Evaluation
<s>[INST]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.[/INST]
Reference Output (Full-weight Model)
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment. The ultimate goal is for the agent to learn a policy, which is a mapping from states to actions that maximizes a long-term reward. In RL, the agent receives feedback in the form of rewards or penalties for each action it takes. The agent uses this feedback to learn which actions lead to thebest long-term outcomes. The agent's goal is to learn a policy that maximizes the total reward over time. The main components of RL are: Environment: It provides the states, actions, and rewards. Agent: It interacts with the environment, selects actions, and learns from the rewards. State: It represents the current situation of the environment. Action: It's the decision made by the agent. Reward: It's the feedback the agent receives for its actions – positive rewards encourage the agent to repeat the action, while negative rewards discourage it. Let's consider a real-world example to understand RL better: a Q-learning agent playing a simple game of Piet Mondrian's grid world. Piet Mondrian's grid world is a grid with rewards placed at specific locations. The agent's goal is to reach the highest reward, which is located at the bottom-right corner of the grid: Piet_Mondrian_grid_world The agent explores the environment and gathers information about state-action values (Q-values). It starts at the upper-left corner (state S0) and exploits actions with the highest Q-values. For example, it moves right (Action A1) to reach state S1. It receives a reward (-1) for moving away from the goal. The agent's current Q-values could look like this: markdown Copy Q(S0, A0) = 0 (Move up) Q(S0, A1) = -1 (Move right) Q(S1, A0) = 0 (Stay) Q(S1, A1) = 0 (Move right) Q(S2, A0) = 1 (Move down) The agent observes the reward and updates its Q-values using the Q-learning update rule: markdown Copy Q(S, A) = Q(S, A) + α * (R + γ * max(Q(S', A')) - Q(S, A)) where α is the learning rate, R is the reward, γ is the discount factor, S' is the next state, and A' is the action with the highest Q-value in the next state. The agent continues exploring the environment and updating its Q-values until it reaches the goal (the bottom-right corner) and the learned policy maximizes the total reward. This example illustrates how an agent learns to navigate an environment using reinforcement learning, ultimately achieving the best possible long-term outcome by learning from the rewards and feedback it receives. Utilizing RL in real-world applications like robotics, gaming, navigation, and more can lead to optimal decision-making in complex environments.
Quantized Model Output
text
1 Reinforcement Learning (RL) is a type of Machine Learning (ML) where an agent learns to make decisions by interacting with its environment. The goal is for the agent to maximize a numerical reward signal, which is received after each action, to learn a policy that maps states to actions. The agent's ultimate objective is to find a policy that maximizes the total reward over time.
23The agent learns from the consequences of its actions, rather than from being explicitly taught, making it different from supervised learning. The learning process can be broken down into the following steps:
451. **State**: The agent perceives the current state of the environment.
62. **Action**: Based on its current policy, the agent selects an action to take.
73. **Reward**: The environment responds with a reward or penalty, depending on the action's outcome.
84. **Transition**: The environment transitions to a new state.
910The agent uses this feedback to update its policy, which can be done using various RL algorithms such as Q-Learning, Deep Q-Networks (DQN), or Policy Gradients.
1112A real-world example of reinforcement learning is teaching a robot to navigate a maze and collect as many bananas as possible. The robot's state would be the current position in the maze, and its actions would be moving in different directions. The reward would be the number of bananas collected. The agent would learn the optimal policy by interacting with the maze, receiving rewards for collecting bananas and penalties for hitting the walls. Over time, the agent would learn the best path to take to maximize its reward.
1314Another example is Google's DeepMind using reinforcement learning to train an agent to play Atari games. The agent learned to play games like Breakout and Space Invaders by interacting with the game environment and learning to maximize its score. This demonstrates the power of reinforcement learning in solving complex tasks that would be difficult to program manually.
Evaluation Metrics
Metric
Value
ROUGE-L F1
0.3009
BLEU
0.1268
Cosine Similarity
0.897
BERTScore F1
0.2305
Higher ROUGE and BLEU scores indicate closer alignment with the original output.
Interpretation:
The quantized model output shows limited similarity to the full-weight model.
