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cosmic-lightning

Text Generation
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English
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llm
language-model
open-source
instruction-following
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@@ -4,229 +4,227 @@ license: apache-2.0
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  library_name: transformers
5
  tags:
6
  - llm
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- - language-model
8
- - open-source
9
- - instruction-following
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- widget:
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- - text: "What is machine learning?"
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- example_title: "Explanation"
13
- - text: "Write a Python function to reverse a string."
14
- example_title: "Code Generation"
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- - text: "Summarize: The rapid advancement of AI has transformed industries worldwide, enabling automation, improved decision-making, and new forms of human-computer interaction."
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- example_title: "Summarization"
17
- ---
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-
19
- # Cosmic Lightning
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-
21
- **A blazingly fast, open-source language model optimized for efficiency and versatility.**
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-
23
- Cosmic Lightning is a compact yet capable language model designed for efficient inference across a wide range of tasks—from code generation and summarization to reasoning and creative writing. Built with a focus on speed and adaptability, it delivers strong performance without demanding extensive computational resources.
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-
25
- ## Model Details
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-
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- - **Model Type:** Transformer-based Language Model
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- - **Architecture:** Optimized causal language model
29
- - **Parameters:** Compact architecture optimized for edge and cloud inference
30
- - **Training Data:** Diverse multilingual and code datasets
31
- - **Language(s):** English, with support for multilingual context
32
- - **License:** Apache 2.0
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- - **Developed by:** Cosmic Stack
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- - **Repository:** [Hugging Face Hub](https://huggingface.co/cosmicstack/cosmic-lightning)
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-
36
- ## Intended Use
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-
38
- ### Direct Use
39
-
40
- Cosmic Lightning is designed for:
41
- - **Text Generation:** Generate coherent, contextually relevant text for various domains
42
- - **Code Generation:** Write code snippets, functions, and scripts
43
- - **Summarization:** Condense documents, articles, and long-form content
44
- - **Question Answering:** Answer factual and analytical questions
45
- - **Instruction Following:** Execute tasks from natural language prompts
46
-
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- ### Example Usage
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-
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- ```python
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- from transformers import AutoTokenizer, AutoModelForCausalLM
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- import torch
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-
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- # Load the model and tokenizer
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- model_name = "cosmicstack/cosmic-lightning"
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- tokenizer = AutoTokenizer.from_pretrained(model_name)
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- model = AutoModelForCausalLM.from_pretrained(
57
- model_name,
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- torch_dtype=torch.float16,
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- device_map="auto"
60
- )
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-
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- # Generate text
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- prompt = "Explain quantum computing in simple terms:"
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- inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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-
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- with torch.no_grad():
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- outputs = model.generate(
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- **inputs,
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- max_length=256,
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- temperature=0.7,
71
- top_p=0.9,
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- do_sample=True
73
- )
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-
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- response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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- print(response)
77
- ```
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-
79
- ### Downstream Use
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-
81
- You can fine-tune Cosmic Lightning for domain-specific tasks:
82
-
83
- ```python
84
- from transformers import Trainer, TrainingArguments
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-
86
- training_args = TrainingArguments(
87
- output_dir="./cosmic-lightning-finetuned",
88
- num_train_epochs=3,
89
- per_device_train_batch_size=4,
90
- learning_rate=2e-5,
91
- save_strategy="epoch",
92
- )
93
-
94
- trainer = Trainer(
95
- model=model,
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- args=training_args,
97
- train_dataset=train_dataset,
98
- )
99
-
100
- trainer.train()
101
- ```
102
-
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- ## Performance
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-
105
- Cosmic Lightning achieves strong results on standard benchmarks:
106
- - Efficient inference: Optimized for both latency and throughput
107
- - Versatile reasoning: Handles diverse task types with consistent quality
108
- - Resource-efficient: Runs on consumer-grade GPUs and CPUs
109
-
110
- ## Bias, Risks, and Limitations
111
-
112
- ### Known Limitations
113
-
114
- - **Knowledge Cutoff:** Training data has a knowledge cutoff; may not reflect recent events or developments
115
- - **Factual Accuracy:** While generally reliable, the model can generate plausible-sounding but incorrect information (hallucinations)
116
- - **Context Length:** Performance may degrade on very long documents near the token limit
117
- - **Domain Specificity:** Best performance on general tasks; domain-specific knowledge may be limited
118
-
119
- ### Recommendations
120
-
121
- - **Fact-Checking:** Verify factual claims, especially for high-stakes applications
122
- - **Prompt Engineering:** Use clear, specific prompts for better results
123
- - **Temperature Tuning:** Adjust temperature based on task—lower for factuality, higher for creativity
124
- - **Evaluation:** Always evaluate outputs for your specific use case
125
-
126
- ### Out-of-Scope Uses
127
-
128
- - **Autonomous High-Stakes Decisions:** Do not rely solely on this model for critical decisions (medical, legal, financial)
129
- - **Generating Harmful Content:** Not intended for generating hate speech, violence, or illegal content
130
- - **Impersonation:** Should not be used to impersonate real individuals or organizations
131
-
132
- ## Training Details
133
-
134
- ### Training Data
135
-
136
- Cosmic Lightning was trained on a diverse corpus including:
137
- - High-quality web text and documentation
138
- - Code repositories and technical documentation
139
- - Multilingual text resources
140
-
141
- The model underwent careful curation to ensure quality and diversity while maintaining safety standards.
142
-
143
- ### Training Procedure
144
-
145
- - **Optimization:** Standard language modeling with next-token prediction
146
- - **Distributed Training:** Optimized for efficient large-scale training
147
- - **Evaluation:** Continuous evaluation on standard benchmarks during training
148
-
149
- ## Hardware Requirements
150
-
151
- ### Minimum Requirements
152
-
153
- - **GPU Memory:** 8GB VRAM (FP16) for inference; 16GB+ for fine-tuning
154
- - **RAM:** 4GB system RAM
155
- - **Storage:** ~5-10GB for model weights and dependencies
156
-
157
- ### Recommended Setup
158
-
159
- - **GPU:** NVIDIA GPU with CUDA support (H100, A100, RTX 4090 for optimal performance)
160
- - **Framework:** PyTorch 2.0+
161
- - **Transformers:** Version 4.30+
162
-
163
- ## How to Get Started
164
-
165
- ### Installation
166
-
167
- ```bash
168
- pip install transformers torch
169
- ```
170
-
171
- ### Quick Start
172
-
173
- ```python
174
- from transformers import pipeline
175
-
176
- # Use the high-level pipeline API
177
- generator = pipeline("text-generation", model="cosmicstack/cosmic-lightning")
178
-
179
- result = generator(
180
- "Once upon a time",
181
- max_length=100,
182
- num_return_sequences=1,
183
- )
184
- print(result[0]["generated_text"])
185
- ```
186
-
187
- ### Using with Ollama (Local Inference)
188
-
189
- ```bash
190
- ollama pull cosmicstack/cosmic-lightning
191
- ollama run cosmicstack/cosmic-lightning "Explain photosynthesis"
192
- ```
193
-
194
- ## Citation
195
-
196
- If you use Cosmic Lightning in your work, please cite:
197
-
198
- ```bibtex
199
- @misc{cosmic-lightning-2026,
200
- title={Cosmic Lightning: A Compact Language Model for Efficient Inference},
201
- author={Cosmic Stack},
202
- year={2026},
203
- publisher={Hugging Face}
204
- }
205
- ```
206
-
207
- ## Environmental Impact
208
-
209
- Training this model required significant computational resources. For details on environmental impact and carbon emissions, see our [environmental impact report](https://cosmicstack.ai/sustainability).
210
-
211
- ## More Information
212
-
213
- - **GitHub:** [Cosmic Stack GitHub](https://github.com/cosmicstack)
214
- - **Documentation:** [Full Documentation](https://docs.cosmicstack.ai)
215
- - **Community:** Join our [Discord community](https://discord.gg/cosmicstack)
216
-
217
- ## Model Card Contact
218
-
219
- For questions, feedback, or improvements to this model card:
220
- - **Email:** models@cosmicstack.ai
221
- - **Issues:** [GitHub Issues](https://github.com/cosmicstack/cosmic-lightning/issues)
222
-
223
- ---
224
-
225
- **Version:** 1.0
226
- **Last Updated:** October 2026
227
- **License:** Apache 2.0
228
- )
229
- )
230
- )
231
- )
232
- )
 
4
  library_name: transformers
5
  tags:
6
  - llm
7
+ - language-model
8
+ - open-source
9
+ - instruction-following
10
+ widget:
11
+ - text: "What is machine learning?"
12
+ example_title: "Explanation"
13
+ - text: "Write a Python function to reverse a string."
14
+ example_title: "Code Generation"
15
+ - text: "Summarize: The rapid advancement of AI has transformed industries worldwide, enabling automation, improved decision-making, and new forms of human-computer interaction."
16
+ example_title: "Summarization"
17
+ ---
18
+
19
+ # Cosmic Lightning
20
+
21
+ **A blazingly fast, open-source language model optimized for efficiency and versatility.**
22
+
23
+ Cosmic Lightning is a compact yet capable language model designed for efficient inference across a wide range of tasks—from code generation and summarization to reasoning and creative writing. Built with a focus on speed and adaptability, it delivers strong performance without demanding extensive computational resources.
24
+
25
+ ## Model Details
26
+
27
+ - **Model Type:** Transformer-based Language Model
28
+ - **Architecture:** Optimized causal language model
29
+ - **Parameters:** Compact architecture optimized for edge and cloud inference
30
+ - **Training Data:** Diverse multilingual and code datasets
31
+ - **Language(s):** English, with support for multilingual context
32
+ - **License:** Apache 2.0
33
+ - **Developed by:** Cosmic Stack
34
+ - **Repository:** [Hugging Face Hub](https://huggingface.co/cosmicstack/cosmic-lightning)
35
+
36
+ ## Intended Use
37
+
38
+ ### Direct Use
39
+
40
+ Cosmic Lightning is designed for:
41
+ - **Text Generation:** Generate coherent, contextually relevant text for various domains
42
+ - **Code Generation:** Write code snippets, functions, and scripts
43
+ - **Summarization:** Condense documents, articles, and long-form content
44
+ - **Question Answering:** Answer factual and analytical questions
45
+ - **Instruction Following:** Execute tasks from natural language prompts
46
+
47
+ ### Example Usage
48
+
49
+ ```python
50
+ from transformers import AutoTokenizer, AutoModelForCausalLM
51
+ import torch
52
+
53
+ # Load the model and tokenizer
54
+ model_name = "cosmicstack/cosmic-lightning"
55
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
56
+ model = AutoModelForCausalLM.from_pretrained(
57
+ model_name,
58
+ torch_dtype=torch.float16,
59
+ device_map="auto",
60
+ )
61
+
62
+ # Generate text
63
+ prompt = "Explain quantum computing in simple terms:"
64
+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
65
+
66
+ with torch.no_grad():
67
+ outputs = model.generate(
68
+ **inputs,
69
+ max_new_tokens=256,
70
+ temperature=0.7,
71
+ top_p=0.9,
72
+ do_sample=True,
73
+ )
74
+
75
+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
76
+ print(response)
77
+ ```
78
+
79
+ ### Downstream Use
80
+
81
+ You can fine-tune Cosmic Lightning for domain-specific tasks:
82
+
83
+ ```python
84
+ from transformers import Trainer, TrainingArguments
85
+
86
+ # Requires a prepared tokenized training dataset named train_dataset.
87
+ training_args = TrainingArguments(
88
+ output_dir="./cosmic-lightning-finetuned",
89
+ num_train_epochs=3,
90
+ per_device_train_batch_size=4,
91
+ learning_rate=2e-5,
92
+ save_strategy="epoch",
93
+ )
94
+
95
+ trainer = Trainer(
96
+ model=model,
97
+ args=training_args,
98
+ train_dataset=train_dataset,
99
+ )
100
+
101
+ trainer.train()
102
+ ```
103
+
104
+ ## Performance
105
+
106
+ Cosmic Lightning achieves strong results on standard benchmarks:
107
+ - Efficient inference: Optimized for both latency and throughput
108
+ - Versatile reasoning: Handles diverse task types with consistent quality
109
+ - Resource-efficient: Runs on consumer-grade GPUs and CPUs
110
+
111
+ ## Bias, Risks, and Limitations
112
+
113
+ ### Known Limitations
114
+
115
+ - **Knowledge Cutoff:** Training data has a knowledge cutoff; may not reflect recent events or developments
116
+ - **Factual Accuracy:** While generally reliable, the model can generate plausible-sounding but incorrect information (hallucinations)
117
+ - **Context Length:** Performance may degrade on very long documents near the token limit
118
+ - **Domain Specificity:** Best performance on general tasks; domain-specific knowledge may be limited
119
+
120
+ ### Recommendations
121
+
122
+ - **Fact-Checking:** Verify factual claims, especially for high-stakes applications
123
+ - **Prompt Engineering:** Use clear, specific prompts for better results
124
+ - **Temperature Tuning:** Adjust temperature based on task—lower for factuality, higher for creativity
125
+ - **Evaluation:** Always evaluate outputs for your specific use case
126
+
127
+ ### Out-of-Scope Uses
128
+
129
+ - **Autonomous High-Stakes Decisions:** Do not rely solely on this model for critical decisions (medical, legal, financial)
130
+ - **Generating Harmful Content:** Not intended for generating hate speech, violence, or illegal content
131
+ - **Impersonation:** Should not be used to impersonate real individuals or organizations
132
+
133
+ ## Training Details
134
+
135
+ ### Training Data
136
+
137
+ Cosmic Lightning was trained on a diverse corpus including:
138
+ - High-quality web text and documentation
139
+ - Code repositories and technical documentation
140
+ - Multilingual text resources
141
+
142
+ The model underwent careful curation to ensure quality and diversity while maintaining safety standards.
143
+
144
+ ### Training Procedure
145
+
146
+ - **Optimization:** Standard language modeling with next-token prediction
147
+ - **Distributed Training:** Optimized for efficient large-scale training
148
+ - **Evaluation:** Continuous evaluation on standard benchmarks during training
149
+
150
+ ## Hardware Requirements
151
+
152
+ ### Minimum Requirements
153
+
154
+ - **GPU Memory:** 8GB VRAM (FP16) for inference; 16GB+ for fine-tuning
155
+ - **RAM:** 4GB system RAM
156
+ - **Storage:** ~5-10GB for model weights and dependencies
157
+
158
+ ### Recommended Setup
159
+
160
+ - **GPU:** NVIDIA GPU with CUDA support (H100, A100, RTX 4090 for optimal performance)
161
+ - **Framework:** PyTorch 2.0+
162
+ - **Transformers:** Version 4.30+
163
+
164
+ ## How to Get Started
165
+
166
+ ### Installation
167
+
168
+ ```bash
169
+ pip install transformers torch accelerate
170
+ ```
171
+
172
+ ### Quick Start
173
+
174
+ ```python
175
+ from transformers import pipeline
176
+
177
+ # Use the high-level pipeline API
178
+ generator = pipeline("text-generation", model="cosmicstack/cosmic-lightning")
179
+
180
+ result = generator(
181
+ "Once upon a time",
182
+ max_new_tokens=100,
183
+ num_return_sequences=1,
184
+ )
185
+ print(result[0]["generated_text"])
186
+ ```
187
+
188
+ ### Using with Ollama (Local Inference)
189
+
190
+ ```bash
191
+ ollama pull cosmicstack/cosmic-lightning
192
+ ollama run cosmicstack/cosmic-lightning "Explain photosynthesis"
193
+ ```
194
+
195
+ ## Citation
196
+
197
+ If you use Cosmic Lightning in your work, please cite:
198
+
199
+ ```bibtex
200
+ @misc{cosmic-lightning-2026,
201
+ title={Cosmic Lightning: A Compact Language Model for Efficient Inference},
202
+ author={Cosmic Stack},
203
+ year={2026},
204
+ publisher={Hugging Face}
205
+ }
206
+ ```
207
+
208
+ ## Environmental Impact
209
+
210
+ Training this model required significant computational resources. For details on environmental impact and carbon emissions, see our [environmental impact report](https://cosmicstack.ai/sustainability).
211
+
212
+ ## More Information
213
+
214
+ - **GitHub:** [Cosmic Stack GitHub](https://github.com/cosmicstack)
215
+ - **Documentation:** [Full Documentation](https://docs.cosmicstack.ai)
216
+ - **Community:** Join our [Discord community](https://discord.gg/cosmicstack)
217
+
218
+ ## Model Card Contact
219
+
220
+ For questions, feedback, or improvements to this model card:
221
+ - **Email:** models@cosmicstack.ai
222
+ - **Issues:** [GitHub Issues](https://github.com/cosmicstack/cosmic-lightning/issues)
223
+
224
+ ---
225
+
226
+ **Version:** 1.0
227
+ **Last Updated:** October 2026
228
+ **License:** Apache 2.0
229
+
230
+ ````