Instructions to use mtzig/lltransformer-linear-test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mtzig/lltransformer-linear-test1 with Transformers:

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

pipe = pipeline("text-generation", model="mtzig/lltransformer-linear-test1")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mtzig/lltransformer-linear-test1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mtzig/lltransformer-linear-test1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mtzig/lltransformer-linear-test1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mtzig/lltransformer-linear-test1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mtzig/lltransformer-linear-test1

SGLang

How to use mtzig/lltransformer-linear-test1 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 "mtzig/lltransformer-linear-test1" \
    --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": "mtzig/lltransformer-linear-test1",
		"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 "mtzig/lltransformer-linear-test1" \
        --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": "mtzig/lltransformer-linear-test1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mtzig/lltransformer-linear-test1 with Docker Model Runner:
```
docker model run hf.co/mtzig/lltransformer-linear-test1
```

mtzig commited on Sep 27, 2025

Commit

7b70a46

verified ·

1 Parent(s): cd53884

End of training: push final checkpoint

Browse files

Files changed (3) hide show

all_results.json +15 -0
train_results.json +15 -0
trainer_state.json +0 -0

all_results.json ADDED Viewed

	@@ -0,0 +1,15 @@

+{
+    "epoch": 0.9997401507125867,
+    "eval_epoch": 0.9997401507125867,
+    "eval_eval_loss": 4.379289150238037,
+    "eval_eval_runtime": 88.5733,
+    "eval_eval_samples_per_second": 21.417,
+    "eval_eval_steps_per_second": 5.363,
+    "eval_perplexity": 79.78130072463807,
+    "total_flos": 1.503421305716736e+17,
+    "train_loss": 4.916973250231068,
+    "train_runtime": 23463.9242,
+    "train_samples": 200115,
+    "train_samples_per_second": 8.529,
+    "train_steps_per_second": 0.133
+}

train_results.json ADDED Viewed

	@@ -0,0 +1,15 @@

+{
+    "epoch": 0.9997401507125867,
+    "eval_epoch": 0.9997401507125867,
+    "eval_eval_loss": 4.379289150238037,
+    "eval_eval_runtime": 88.5733,
+    "eval_eval_samples_per_second": 21.417,
+    "eval_eval_steps_per_second": 5.363,
+    "eval_perplexity": 79.78130072463807,
+    "total_flos": 1.503421305716736e+17,
+    "train_loss": 4.916973250231068,
+    "train_runtime": 23463.9242,
+    "train_samples": 200115,
+    "train_samples_per_second": 8.529,
+    "train_steps_per_second": 0.133
+}

trainer_state.json ADDED Viewed

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