Adapter_Mistral-7B-Instruct-v0.3-co-sft-qlora

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<details><summary>See axolotl config</summary>

axolotl version: 0.13.0.dev0

# 1. Base Model & Tokenizer
base_model: mistralai/Mistral-7B-Instruct-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
hub_model_id: AiAF/Adapter_Mistral-7B-Instruct-v0.3-co-sft-qlora

# 2. LoRA / QLoRA Configuration
load_in_8bit: false
load_in_4bit: true
adapter: qlora
# Matching gemma2 config for potentially better results
lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

# 3. Dataset Configuration
datasets:
  - path: .
    type: chat_template
    data_files: ./co-sft-dataset.jsonl
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    chat_template: jinja
    chat_template_jinja: |
      {{ bos_token }}

      {# 1) Collect all system messages and join them #}
      {% set sys = (
        messages
        | selectattr('role', 'equalto', 'system')
        | map(attribute='content')
        | list
      ) %}
      {% set sys_joined = sys | reject('equalto', None) | map('trim') | select | join('\n\n') %}

      {# 2) Emit conversation without 'system' role; inject system text into the FIRST human turn #}
      {% set injected = namespace(done=false) %}
      {% for m in messages if m['role'] != 'system' %}
        {% if m['role'] == 'human' %}
          {% if not injected.done and sys_joined %}
            {{ '[INST] ' + (sys_joined ~ '\n\n' ~ (m['content'] | trim)) + ' [/INST]' }}
            {% set injected.done = true %}
          {% else %}
            {{ '[INST] ' + (m['content'] | trim) + ' [/INST]' }}
          {% endif %}
        {% elif m['role'] == 'assistant' %}
          {{ ' ' + (m['content'] | trim) + eos_token }}
        {% endif %}
      {% endfor %}

    roles_to_train: ["assistant"]

# 4. Training Parameters
sequence_len: 2048
sample_packing: true
val_set_size: 0.05
num_epochs: 10
dataset_prepared_path: last_run_prepared
gradient_accumulation_steps: 1 #4
micro_batch_size: 32 #2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: true
tf32: true
gradient_checkpointing: true

# 5. Saving and Evaluation Strategy
output_dir: ./outputs/sft/Mistral-7B-Instruct-v0.3-co-sft-qlora
logging_steps: 10
evals_per_epoch: 5
saves_per_epoch: 10
save_total_limit: 100

# 6. W&B Logging
wandb_project: "co-sft"
wandb_name: "Mistral-7B-Instruct-v0.3-co-sft-qlora-co-sft"
wandb_log_model: "false"
wandb_run_id: "Mistral-7B-Instruct-v0.3-co-sft-qlora"

# 7. Special Tokens
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

</details><br>

Adapter_Mistral-7B-Instruct-v0.3-co-sft-qlora

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.9397
• Memory/max Active (gib): 46.02
• Memory/max Allocated (gib): 46.02
• Memory/device Reserved (gib): 53.04

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 41
• training_steps: 1390

Training results

Framework versions

• PEFT 0.17.1
• Transformers 4.56.1
• Pytorch 2.7.1+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1