Instructions to use Tippawan/tinyllama-codeHtml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tippawan/tinyllama-codeHtml with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "Tippawan/tinyllama-codeHtml")

Transformers

How to use Tippawan/tinyllama-codeHtml with Transformers:

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

pipe = pipeline("text-generation", model="Tippawan/tinyllama-codeHtml")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tippawan/tinyllama-codeHtml", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Tippawan/tinyllama-codeHtml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tippawan/tinyllama-codeHtml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tippawan/tinyllama-codeHtml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tippawan/tinyllama-codeHtml

SGLang

How to use Tippawan/tinyllama-codeHtml 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 "Tippawan/tinyllama-codeHtml" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tippawan/tinyllama-codeHtml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Tippawan/tinyllama-codeHtml" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tippawan/tinyllama-codeHtml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tippawan/tinyllama-codeHtml with Docker Model Runner:
```
docker model run hf.co/Tippawan/tinyllama-codeHtml
```

Tippawan commited on Oct 20, 2025

Commit

5daca33

verified ·

1 Parent(s): 9d8cc01

End of training

Browse files

Files changed (4) hide show

README.md +1 -1
all_results.json +4 -4
train_results.json +4 -4
trainer_state.json +4 -4

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ should probably proofread and complete it, then remove this comment. -->
 # tinyllama-codeHtml
-This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
 ## Model description

 # tinyllama-codeHtml
+This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the colors dataset.
 ## Model description

all_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 3.0,
     "total_flos": 1422043529084928.0,
-    "train_loss": 0.12496965477075526,
-    "train_runtime": 258.8654,
-    "train_samples_per_second": 5.795,
-    "train_steps_per_second": 0.73
 }

 {
     "epoch": 3.0,
     "total_flos": 1422043529084928.0,
+    "train_loss": 0.0,
+    "train_runtime": 0.0314,
+    "train_samples_per_second": 47715.321,
+    "train_steps_per_second": 6012.131
 }

train_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 3.0,
     "total_flos": 1422043529084928.0,
-    "train_loss": 0.12496965477075526,
-    "train_runtime": 258.8654,
-    "train_samples_per_second": 5.795,
-    "train_steps_per_second": 0.73
 }

 {
     "epoch": 3.0,
     "total_flos": 1422043529084928.0,
+    "train_loss": 0.0,
+    "train_runtime": 0.0314,
+    "train_samples_per_second": 47715.321,
+    "train_steps_per_second": 6012.131
 }

trainer_state.json CHANGED Viewed

@@ -272,10 +272,10 @@
       "epoch": 3.0,
       "step": 189,
       "total_flos": 1422043529084928.0,
-      "train_loss": 0.12496965477075526,
-      "train_runtime": 258.8654,
-      "train_samples_per_second": 5.795,
-      "train_steps_per_second": 0.73
     }
   ],
   "logging_steps": 5,

       "epoch": 3.0,
       "step": 189,
       "total_flos": 1422043529084928.0,
+      "train_loss": 0.0,
+      "train_runtime": 0.0314,
+      "train_samples_per_second": 47715.321,
+      "train_steps_per_second": 6012.131
     }
   ],
   "logging_steps": 5,