Text Generation
Transformers
Safetensors
English
Vietnamese
qwen2
gamedev
godot
json
spawn-point
awq
conversational
text-generation-inference
4-bit precision
Instructions to use smoky1496/SpawnPoint-Coder-7B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smoky1496/SpawnPoint-Coder-7B-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="smoky1496/SpawnPoint-Coder-7B-AWQ") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("smoky1496/SpawnPoint-Coder-7B-AWQ") model = AutoModelForCausalLM.from_pretrained("smoky1496/SpawnPoint-Coder-7B-AWQ", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use smoky1496/SpawnPoint-Coder-7B-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smoky1496/SpawnPoint-Coder-7B-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "smoky1496/SpawnPoint-Coder-7B-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/smoky1496/SpawnPoint-Coder-7B-AWQ
- SGLang
How to use smoky1496/SpawnPoint-Coder-7B-AWQ 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 "smoky1496/SpawnPoint-Coder-7B-AWQ" \ --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": "smoky1496/SpawnPoint-Coder-7B-AWQ", "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 "smoky1496/SpawnPoint-Coder-7B-AWQ" \ --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": "smoky1496/SpawnPoint-Coder-7B-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use smoky1496/SpawnPoint-Coder-7B-AWQ with Docker Model Runner:
docker model run hf.co/smoky1496/SpawnPoint-Coder-7B-AWQ
SpawnPoint-Coder-7B v6: card games (deckbuilders)
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README.md
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4-bit AWQ, runs on a single 12 GB GPU.
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**What's new in v5.2, v5.1 and v5**
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- **v5.2** catches farm pests mentioned only in passing ("game nông trại mùa đông, có cáo", "a farm with a fox around"),
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keeps a named farmer ("a cat farmer"), and leaves a peaceful farm without pests.
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- **v5.1** fixes prompts that name the player only indirectly ("a tank shooting monsters": the tank is now the hero,
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- Fixes from v4: "a wizard shooting skeletons" keeps the shoot ability, "collect coins" is a collect goal, an unnamed
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pickup is a Coin.
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**Needs Spawn Point 0.
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format; use release `v4-release` of this repo with them.
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## Run it
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Sealed sets were written before training and not looked at until the release decision. The first two rows include
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prompts about the genre v4 did not know, so v4 cannot be expected to answer them.
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| benchmark (prompts fully right) | v4 | v5.
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| **sealed set:
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| sealed set:
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from run to run, so read differences of one or
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The 4-bit AWQ files you download, measured the same way:
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| benchmark (prompts fully right) | AWQ build |
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| **sealed set:
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| 30 human-written prompts |
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| sealed set:
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## Limitations
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- Fine-tuned only for the 2D game kinds Spawn Point builds: **platformers (including rescue missions and sword
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fighting), top-down arenas and survivors, bubble shooters, farming games, tower defense
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shooters**. Other genres (3D, RPG systems,
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training data, so expect results no better than a general 7B coder model there.
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- Prompts that do not say the genre can be read either way ("a space shooter" may come out as a side-scrolling
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shooter, "a farm game" as a farming game).
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4-bit AWQ, runs on a single 12 GB GPU.
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**What's new in v6, v5.2, v5.1 and v5**
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- **v6** designs **card games** (Slay the Spire style deckbuilders): "a deckbuilder where a wizard climbs a frozen
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tower full of ice spirits", "game thẻ bài kiểu Slay the Spire, hiệp sĩ đánh lũ xương khô, trùm cuối là Lich". It picks
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the hero and its class (warrior, mage, rogue), the game's own cards and what each does (big hits, sweeps, poison,
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shields, healing...), the foes and how they fight (brutes, guards, casters, swarms) and the boss. "Upgrade cards" in
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a survivor or "key cards" in a shooter stay what they were.
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- **v5.2** catches farm pests mentioned only in passing ("game nông trại mùa đông, có cáo", "a farm with a fox around"),
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keeps a named farmer ("a cat farmer"), and leaves a peaceful farm without pests.
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- **v5.1** fixes prompts that name the player only indirectly ("a tank shooting monsters": the tank is now the hero,
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- Fixes from v4: "a wizard shooting skeletons" keeps the shoot ability, "collect coins" is a collect goal, an unnamed
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pickup is a Coin.
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**Needs Spawn Point 0.4 or later for card games** (0.3 or later for the rest): v5 and later answer in the per-genre
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format. Older Spawn Point versions expect the v4 format; use release `v4-release` of this repo with them.
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## Run it
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Sealed sets were written before training and not looked at until the release decision. The first two rows include
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prompts about the genre v4 did not know, so v4 cannot be expected to answer them.
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| benchmark (prompts fully right) | v4 | v5.2 | v6 (this release) |
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| **sealed set: card games** (16 prompts) | - | 56% | 94% |
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| sealed set: farm pests named in passing (16 prompts) | - | 100% | 100% |
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| sealed set: shooters and the v4 fixes (16 prompts) | 56% | 88% | 88% |
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| dev set: shooters and the v4 fixes (24 prompts) | 54% | 79% | 88% |
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| sealed set: farms, tower defense, cartoon (12 prompts) | 67% | 92% | 83% |
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| 16 prompts: farms, tower defense, melee, cartoon | 75% | 81% | 94% |
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| sealed set: the first nine genres (12 prompts) | 67% | 75% | 83% |
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| 30 human-written prompts | 87% | 87% | 90% |
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| 12 prompts: bubble, rescue, survivor | 92% | 92% | 75% |
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**Where v6 is a little behind v5.2**: the 12-prompt bubble / rescue / survivor set (two prompts, both names in a bubble
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shooter: the shooter or the egg) and the 12-prompt sealed farms / tower defense set (one prompt), and exact synthetic
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briefs (70% against 77%). Results vary by one to three prompts per set from run to run, so read differences of one or
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two prompts as noise.
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Held-out synthetic examples with one exact answer (v5 format only): briefs 70%, tweaks 98%, entity edits 96%.
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The 4-bit AWQ files you download, measured the same way:
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| benchmark (prompts fully right) | AWQ build |
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| **sealed set: card games** (16 prompts) | 94% |
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| 30 human-written prompts | 87% |
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| sealed set: farm pests named in passing (16 prompts) | 100% |
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| sealed set: shooters and the v4 fixes (16 prompts) | 81% |
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## Limitations
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- Fine-tuned only for the 2D game kinds Spawn Point builds: **platformers (including rescue missions and sword
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fighting), top-down arenas and survivors, bubble shooters, farming games, tower defense, side-scrolling
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shooters and card games**. Other genres (3D, RPG systems, racing…) and free-form Godot coding were not part of the
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training data, so expect results no better than a general 7B coder model there.
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- Prompts that do not say the genre can be read either way ("a space shooter" may come out as a side-scrolling
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shooter, "a farm game" as a farming game).
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model-00001-of-00002.safetensors
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