Instructions to use dcostenco/prism-coder-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-14b with Transformers:

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

pipe = pipeline("text-generation", model="dcostenco/prism-coder-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dcostenco/prism-coder-14b")
model = AutoModelForCausalLM.from_pretrained("dcostenco/prism-coder-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dcostenco/prism-coder-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dcostenco/prism-coder-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dcostenco/prism-coder-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dcostenco/prism-coder-14b

SGLang

How to use dcostenco/prism-coder-14b 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 "dcostenco/prism-coder-14b" \
    --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": "dcostenco/prism-coder-14b",
		"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 "dcostenco/prism-coder-14b" \
        --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": "dcostenco/prism-coder-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dcostenco/prism-coder-14b with Docker Model Runner:
```
docker model run hf.co/dcostenco/prism-coder-14b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Prism-Coder 14B — Function Calling + AAC Sibling (32K context)

A fine-tune of Qwen2.5-Coder-14B-Instruct released 2026-05-04 as a sibling to prism-coder-7b. Auto-routed for paid-tier medium-length AAC queries via the Synalux portal — keeps inference local on cloud GPU pool, $0 marginal cost vs Claude/Gemini.

Sibling positioning

Model	Use case	Context	RAM (Q4)
`prism-coder-7b`	iPad consumer AAC, free portal tier	32K	~5 GB
`prism-coder-14b`	Mac/desktop AAC, paid portal tier (medium queries)	32K	~9 GB
`prism-coder-32b` (in flight, Phase 1)	Synalux cloud paid-tier complex queries	32K	~20 GB

Eval (Prism internal, 3-run StdDev 0%)

Metric	Score
BFCL (Prism 64-test)	85.9%
AAC realigned	46/48 (95.8%)
Caregiver targeted	18/20
Emergency QA	13/13
Text correction	14/15
Translation	8/8
Ask AI	5/5

The 14B is NOT explicitly AAC-trained (data was BFCL/tool-calling focused) — its high AAC scores are emergent from Qwen2.5-Coder-14B-Instruct's strong instruct-tuning + format transfer from BFCL training. The 7B sibling explicitly includes AAC SFT data and edges out 14B on caregiver targeted (20/20 vs 18/20) but not on general reasoning.

Berkeley BFCL V4 (in progress)

Handler integration PR open at ShishirPatil/gorilla#1332 supporting prism-coder-14b-FC alongside the 7B/32B/72B variants. Self-run with the official Berkeley toolkit is in progress; numbers will be appended once complete.

Use cases

Synalux portal — paid tier

Tier-aware routing dispatches:

Simple AAC queries → 7B local (cheap, fast)
Medium queries (5-40 words) → 14B local (this model) — stronger reasoning, $0 marginal
Complex queries → Claude Opus / Haiku per tier

This routing alone is estimated to save $190K-210K/year at 10K-user scale vs all-cloud routing.

Self-hosted Mac / desktop AAC

Q4_K_M GGUF (~9 GB) fits on Mac M2/M3/M4 with ≥16 GB RAM. Runs at 15-30 tok/s — comfortable for AAC turns.

Format

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("dcostenco/prism-coder-14b")
m = AutoModelForCausalLM.from_pretrained(
    "dcostenco/prism-coder-14b",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Add 'eat apples' to the food category."}],
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tok(prompt, return_tensors="pt").to(m.device)
out = m.generate(**inputs, max_new_tokens=160, temperature=0.3)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

For Ollama users, a Q4_K_M GGUF is available via the prism-coder:14b tag in the Synalux ops fleet.

Training

Base: Qwen/Qwen2.5-Coder-14B-Instruct
Method: DoRA SFT (resumed from base 14B SFT checkpoint-5000)
Adapter: r=128, alpha=256, lora_dropout=0.05
Schedule: 1 epoch, LR 1e-5 cosine, warmup 5%
Data: glaive-function-calling-v2 + ToolACE + xlam-function-calling-60k + internal v17.1 BFCL (60K rows subsampled, Hammer-style 24% function-masked)
Compute: H100×2 on Modal, ~10h total

License

Apache 2.0. Free for research and commercial use.

Citation

@misc{prism-coder-14b-2026,
  title         = {Prism-Coder 14B: Function Calling + AAC Sibling Fine-Tune of Qwen2.5-Coder-14B},
  author        = {Synalux AI / Dmitri Costenco},
  year          = {2026},
  month         = {May},
  url           = {https://huggingface.co/dcostenco/prism-coder-14b},
  note          = {Sibling 7B model: https://huggingface.co/dcostenco/prism-coder-7b. PR: https://github.com/ShishirPatil/gorilla/pull/1332.}
}