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Sponsored by
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minpeter / LoRA-Llama-3.2-1B-tool-vllm-ci
Model's Last Updated: June 23 2025
Introduction of LoRA-Llama-3.2-1B-tool-vllm-ci
Model Details of LoRA-Llama-3.2-1B-tool-vllm-ci
See axolotl config
axolotl version:
0.11.0.dev0
base_model: meta-llama/Llama-3.2-1B-Instruct
hub_model_id: minpeter/llama-1b-tool-vllm-ci
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
# 0.5k
- path: minpeter/xlam-function-calling-60k-hermes
data_files:
- result.parquet
type: chat_template
chat_template: llama3
field_messages: conversations
message_field_role: from
message_field_content: value
shards: 120
# 0.35k
- path: minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes
data_files:
- result.parquet
type: chat_template
chat_template: llama3
field_messages: conversations
message_field_role: from
message_field_content: value
shards: 15
# 1.2k
- path: minpeter/hermes-function-calling-v1-jsonl
data_files:
- func-calling-singleturn.jsonl
- func-calling.jsonl
type: chat_template
chat_template: llama3
field_messages: conversations
message_field_role: from
message_field_content: value
shards: 3
# 1k
- path: minpeter/apigen-mt-5k-friendli
data_files:
- train.jsonl
type: chat_template
chat_template: llama3
field_messages: messages
message_field_role: role
message_field_content: content
shards: 5
chat_template: llama3
dataset_prepared_path: last_run_prepared
output_dir: ./output
adapter: lora
lora_model_dir:
sequence_len: 4096
pad_to_sequence_len: true
sample_packing: true
val_set_size: 0.05
eval_sample_packing: true
evals_per_epoch: 3
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project: "axolotl"
wandb_entity: "kasfiekfs-e"
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|finetune_right_pad_id|>"
llama-1b-tool-vllm-ci
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the minpeter/xlam-function-calling-60k-hermes, the minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes, the minpeter/hermes-function-calling-v1-jsonl and the minpeter/apigen-mt-5k-friendli datasets. It achieves the following results on the evaluation set:
- Loss: 0.4122
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_8BIT 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: 10
- training_steps: 380
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0 | 0 | 0.9339 |
| 0.3676 | 0.3360 | 64 | 0.4842 |
| 0.493 | 0.6719 | 128 | 0.4455 |
| 0.4702 | 1.0052 | 192 | 0.4302 |
| 0.574 | 1.3412 | 256 | 0.4195 |
| 0.4548 | 1.6772 | 320 | 0.4122 |
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
Runs of minpeter LoRA-Llama-3.2-1B-tool-vllm-ci on huggingface.co
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More Information About LoRA-Llama-3.2-1B-tool-vllm-ci huggingface.co Model
Url of LoRA-Llama-3.2-1B-tool-vllm-ci
LoRA-Llama-3.2-1B-tool-vllm-ci huggingface.co Url
Provider of LoRA-Llama-3.2-1B-tool-vllm-ci huggingface.co
Other API from minpeter
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