Instructions to use BEE-spoke-data/smol_llama-101M-midjourney-messages with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BEE-spoke-data/smol_llama-101M-midjourney-messages with Transformers:

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

pipe = pipeline("text-generation", model="BEE-spoke-data/smol_llama-101M-midjourney-messages")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/smol_llama-101M-midjourney-messages")
model = AutoModelForMultimodalLM.from_pretrained("BEE-spoke-data/smol_llama-101M-midjourney-messages")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BEE-spoke-data/smol_llama-101M-midjourney-messages with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEE-spoke-data/smol_llama-101M-midjourney-messages"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/smol_llama-101M-midjourney-messages",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BEE-spoke-data/smol_llama-101M-midjourney-messages

SGLang

How to use BEE-spoke-data/smol_llama-101M-midjourney-messages 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 "BEE-spoke-data/smol_llama-101M-midjourney-messages" \
    --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": "BEE-spoke-data/smol_llama-101M-midjourney-messages",
		"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 "BEE-spoke-data/smol_llama-101M-midjourney-messages" \
        --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": "BEE-spoke-data/smol_llama-101M-midjourney-messages",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/smol_llama-101M-midjourney-messages with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/smol_llama-101M-midjourney-messages
```

pszemraj commited on Dec 29, 2025

Commit

935e6dc

verified ·

0 Parent(s):

Super-squash branch 'main' using huggingface_hub

Browse files

Files changed (9) hide show

.gitattributes +35 -0
README.md +66 -0
config.json +28 -0
generation_config.json +6 -0
model.safetensors +3 -0
special_tokens_map.json +23 -0
tokenizer.model +3 -0
tokenizer_config.json +42 -0
training_args.bin +3 -0

.gitattributes ADDED Viewed

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README.md ADDED Viewed

	@@ -0,0 +1,66 @@

+---
+license: apache-2.0
+base_model: BEE-spoke-data/smol_llama-101M-GQA
+tags:
+- art
+- text2image
+- prompt
+- prompt generator
+- diffusion util
+metrics:
+- accuracy
+inference:
+  parameters:
+    max_new_tokens: 64
+    do_sample: true
+    temperature: 0.8
+    repetition_penalty: 1.15
+    no_repeat_ngram_size: 4
+    eta_cutoff: 0.001
+    renormalize_logits: true
+widget:
+- text: avocado chair
+  example_title: avocado chair
+- text: A mysterious potato
+  example_title: potato
+pipeline_tag: text-generation
+datasets:
+- pszemraj/midjourney-messages-cleaned
+---
+# smol_llama-101M-midjourney-messages
+Given a 'partial prompt' for a text2image model, this generates additional relevant text to include for a full prompt.
+![example](https://i.imgur.com/f2hzgq1.png)
+dalle3:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/PIBazuqQ1DrTxZbWSay2o.png)
+## Model description
+This model is a fine-tuned version of [BEE-spoke-data/smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) on the `pszemraj/midjourney-messages-cleaned` dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.8431
+- Accuracy: 0.4682
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.00025
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 17056
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
+- lr_scheduler_type: inverse_sqrt
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 1.0

config.json ADDED Viewed

	@@ -0,0 +1,28 @@

+{
+  "_name_or_path": "pszemraj/smol_llama-101M-GQA-midjourney-messages-cleaned-1024-vN",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 1024,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 6,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.36.0.dev0"
+}

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+    "rstrip": false,
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+  "legacy": false,
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+  "padding_side": "right",
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+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": true
+}

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