Instructions to use AlexHung29629/fix_magistral7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/fix_magistral7 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AlexHung29629/fix_magistral7")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlexHung29629/fix_magistral7")
model = AutoModelForCausalLM.from_pretrained("AlexHung29629/fix_magistral7")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlexHung29629/fix_magistral7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/fix_magistral7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexHung29629/fix_magistral7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AlexHung29629/fix_magistral7

SGLang

How to use AlexHung29629/fix_magistral7 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AlexHung29629/fix_magistral7" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexHung29629/fix_magistral7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AlexHung29629/fix_magistral7" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexHung29629/fix_magistral7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AlexHung29629/fix_magistral7 with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/fix_magistral7
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

See axolotl config

axolotl version: 0.11.0.dev0

base_model: AlexHung29629/Magistral-Small-2506
tokenizer_use_mistral_common: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

#unfrozen_parameters:
#  - ^lm_head.+$
#  - ^.+embed_tokens.+$

datasets:
  - path: AlexHung29629/rr-mg-segmented
    type: input_output
remove_unused_columns: false

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

use_tensorboard: true

save_only_model: true
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch_fused
#optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 5e-7
max_grad_norm: 1.0

adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-8

bf16: true
tf32: false

warmup_ratio: 0.05
saves_per_epoch: 1
weight_decay: 0


train_on_inputs: false

flash_attention: true
#deepspeed: /workspace/output/zero3.json

fsdp:
  - full_shard
  - auto_wrap

fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: false
  fsdp_use_orig_params: true
  fsdp_cpu_ram_efficient_loading: true
  fsdp_activation_checkpointing: true
  fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
seed: 42

hub_model_id: AlexHung29629/fix_magistra7
output_dir: /workspace/output/output_dir7
dataset_processes: 0
dataset_prepared_path: /workspace/output/dataset_prepared
torch_compile: false

added_tokens_overrides:
  32: "[ARGS]"
  33: "[CALL_ID]"

fix_magistra7

This model is a fine-tuned version of AlexHung29629/Magistral-Small-2506 on the AlexHung29629/rr-mg-segmented dataset.

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: 5e-07
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 3
training_steps: 76

Training results

Framework versions

Transformers 4.52.4
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: -

Safetensors

Model size

24B params

Tensor type

F32

Model tree for AlexHung29629/fix_magistral7

Base model

AlexHung29629/Magistral-Small-2506

Finetuned

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this model

Finetunes

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AlexHung29629
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fix_magistral7

You need to agree to share your contact information to access this model

fix_magistra7

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for AlexHung29629/fix_magistral7

Dataset used to train AlexHung29629/fix_magistral7