Instructions to use Interchained/imagine-v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Interchained/imagine-v10 with Transformers:

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

pipe = pipeline("text-generation", model="Interchained/imagine-v10")
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("Interchained/imagine-v10")
model = AutoModelForCausalLM.from_pretrained("Interchained/imagine-v10", 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

llama.cpp

How to use Interchained/imagine-v10 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Interchained/imagine-v10:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Interchained/imagine-v10:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Interchained/imagine-v10:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Interchained/imagine-v10:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Interchained/imagine-v10:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Interchained/imagine-v10:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Interchained/imagine-v10:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Interchained/imagine-v10:Q4_K_M

Use Docker

docker model run hf.co/Interchained/imagine-v10:Q4_K_M

LM Studio
Jan

vLLM

How to use Interchained/imagine-v10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Interchained/imagine-v10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Interchained/imagine-v10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Interchained/imagine-v10:Q4_K_M

SGLang

How to use Interchained/imagine-v10 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 "Interchained/imagine-v10" \
    --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": "Interchained/imagine-v10",
		"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 "Interchained/imagine-v10" \
        --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": "Interchained/imagine-v10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Interchained/imagine-v10 with Ollama:
```
ollama run hf.co/Interchained/imagine-v10:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Interchained/imagine-v10 with Docker Model Runner:
```
docker model run hf.co/Interchained/imagine-v10:Q4_K_M
```

Lemonade

How to use Interchained/imagine-v10 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Interchained/imagine-v10:Q4_K_M

Run and chat with the model

lemonade run user.imagine-v10-Q4_K_M

List all available models

lemonade list

Atomic Chat

imagine-v10 / README.md

Interchained

Update README.md

a910fe3 verified 14 days ago

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---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- text-to-sql
- postgresql
- code-generation
base_model: Interchained/imagine-v9
---
<div align="center">

# 🧠 imagine-v10

### 100% on held-out. Zero hallucinations. Analytical SQL that actually works.

**Natural language in. Correct PostgreSQL out. ~1B parameters. No GPU. No API key. No metered inference.**

[![eval](https://img.shields.io/badge/heldout_eval-40%2F40_100%25-3ecf8e?style=flat-square)](#evaluation)
[![gate](https://img.shields.io/badge/execution_gate-68%2F68-3ecf8e?style=flat-square)](#execution-gate)
[![protocol](https://img.shields.io/badge/protocol_compliance-100%25-3ecf8e?style=flat-square)](#evaluation)
[![hallucinations](https://img.shields.io/badge/invented_columns-0-3ecf8e?style=flat-square)](#evaluation)
[![runtime](https://img.shields.io/badge/runtime-CPU_only-f5a524?style=flat-square)](#local-first)
[![licence](https://img.shields.io/badge/license-Apache--2.0-8957e5?style=flat-square)](#status)

*Part of project `imagine` — Interchained*

</div>

---

## The failure that built v10

v9 was asked for a LEFT JOIN analytical query:

```sql
-- "List every customer, their total completed spending, sort by spending desc"
```

It returned:

```sql
SELECT c.id, c.name, COALESCE(o.total, 0) AS total_spending
FROM customers c
LEFT JOIN orders o ON c.id
-- ...truncated. Dead.
```

The correct answer:

```sql
SELECT c.id, c.name, COALESCE(SUM(o.total), 0) AS total_spending
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id AND o.status = 'completed'
GROUP BY c.id, c.name
ORDER BY total_spending DESC, c.id ASC;
```

v9 couldn't do multi-clause analytical queries. **v10 can.**

---

## 🎯 What v10 adds

Where v9 added write capability (`INSERT`/`UPDATE`/`DELETE`), v10 adds **complex analytical queries**:

- **LEFT JOIN + GROUP BY + COALESCE** — the exact v9 failure mode, now handled
- **HAVING filters** — post-aggregation conditions
- **Subquery comparisons** — nested analytical logic
- **Complex ORDER BY** — multi-column, expression-based sorting
- **Multi-table JOINs** — beyond simple two-table patterns

---

## 📊 The numbers

| Model | Held-out (telemetry) | Protocol | Execute rate | Invented cols |
|---|---|---|---|---|
| imagine-v8 | 50.0% (20/40) | 100% | — | — |
| imagine-v9 | 90.0% (36/40) | 100% | 97.5% | 0 |
| **imagine-v10** | **100.0% (40/40)** | **100%** | **100%** | **0** |

**Perfect score.** Zero wrong answers. Zero hallucinations. Zero refusals.

---

## 🧬 How v10 was built

Two-stage fine-tuning from `Interchained/imagine-v9`:

### Stage 1 — Full fine-tune (smoke)

- **Corpus:** 3,216 admitted pairs (99.94% gate admit rate)
- **New:** Analytical query templates (LEFT JOIN aggregations, HAVING, subqueries)
- **Epochs:** 3, LR 1e-5, cosine schedule
- **Final loss:** 0.0052 (v9 was 0.0320 — **6x better**)
- **Time:** 4.1 min on H200, 26k tok/s

### Stage 2 — LoRA refinement

- **Base:** v10-smoke checkpoint
- **Rank:** 16, LR 5e-6
- **Epochs:** 3
- **Final loss:** 0.0244
- **Time:** 5.4 min on H200
- Adapter folded into full checkpoint

### The corpus

3,218 candidates forged, 3,216 admitted:

| Schema | Templates | Writes | Total |
|---|---|---|---|
| shop | 356 | 388 | 744 |
| clinic | 545 | 290 | 835 |
| library | 481 | 257 | 738 |
| fleet | 297 | 226 | 523 |
| audit | 252 | 126 | 378 |

**Nothing enters training that a live database hasn't agreed with.**

---

## 🔬 The execution gate

Every candidate goes through L0–L4:

```
candidate SQL ──► L0  real PostgreSQL parser     not a regex
                 L1  read-only + bounded          no writes, no sleeps
                 L2  EXPLAIN on live schema       hallucinations die HERE
                 L3  execute, timed, capped       real rows
                 L4  SAME ANSWER as ref?          ◄── the one that matters
                                              ▼
                                       admitted to corpus
```

68/68 gate tests passing. The gate is the truth authority — not a bigger model, not vibes.

---

## 📦 Output protocol

SQL wrapped in sentinel blocks:

```
<<<SQL>>>
SELECT c.id, c.name, COALESCE(SUM(o.total), 0) AS total_spending
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id AND o.status = 'completed'
GROUP BY c.id, c.name
ORDER BY total_spending DESC, c.id ASC;
<<<END>>>
```

Can also refuse:

| block | meaning |
|---|---|
| `<<<SQL>>>` | here is your query |
| `<<<UNANSWERABLE>>>` | this schema cannot answer that |
| `<<<CLARIFY>>>` | ambiguous — here's what's missing |

> ⚠️ **A truncated generation is not an answer.** An unterminated block extracts to **nothing**.

---

## 💻 Loading v10

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Interchained/imagine-v10"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
# Ask for an analytical query
prompt = """Schema:
customers(id, name)
orders(id, customer_id, total, status)
List every customer ID, name, and total completed spending.
Include customers with no completed orders (show 0).
Sort by spending descending, then ID ascending."""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(inputs["input_ids"], max_new_tokens=256, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

---

## 🎯 Identity

The deployed model identity is **Imagine**.

Built and fine-tuned by **Interchained**.

DeepSeek-Coder is part of the upstream lineage (via v8 → v9 → v10), but the deployed identity is Imagine.

---

## ⚡ Local-first

Runs on your hardware. No API key. No metered inference. No cloud dependency.

---

## 📐 The rules

**1 · Don't write a verifier — the engine already shipped one.**
Real parser. Real planner. Real rows.

**2 · Assert the property, not a proxy.**
Execution accuracy, not string similarity.

**3 · Schema goes in the prompt, not in the weights.**
The model learns *"read the schema you were handed"* — not memorise ours.

---

## ⚠️ Limitations

v10 is a research checkpoint. It may still:

- generate incorrect SQL on novel patterns
- misunderstand ambiguous requests
- produce writes with wrong `WHERE` clauses — **always review before executing**

Generated SQL should be reviewed before use in production. This applies doubly to writes.

---

## 🔒 Security

Do not rely on model behavior alone for database safety. Production systems should enforce:

- least-privilege database roles
- statement timeouts and row limits
- query validation and schema restrictions
- application-level authorization
- audit logging
- **human approval for all write statements**

---

<div align="center">

**Built by [Interchained](https://Interchained.org)** · ownership at every layer, including the model

`3 > 1` — Mark drives, oracle points, Muse builds

</div>

	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- text-to-sql
	- postgresql
	- code-generation
	base_model: Interchained/imagine-v9
	---

	<div align="center">

	# 🧠 imagine-v10

	### 100% on held-out. Zero hallucinations. Analytical SQL that actually works.

	Natural language in. Correct PostgreSQL out. ~1B parameters. No GPU. No API key. No metered inference.

	[![eval](https://img.shields.io/badge/heldout_eval-40%2F40_100%25-3ecf8e?style=flat-square)](#evaluation)
	[![gate](https://img.shields.io/badge/execution_gate-68%2F68-3ecf8e?style=flat-square)](#execution-gate)
	[![protocol](https://img.shields.io/badge/protocol_compliance-100%25-3ecf8e?style=flat-square)](#evaluation)
	[![hallucinations](https://img.shields.io/badge/invented_columns-0-3ecf8e?style=flat-square)](#evaluation)
	[![runtime](https://img.shields.io/badge/runtime-CPU_only-f5a524?style=flat-square)](#local-first)
	[![licence](https://img.shields.io/badge/license-Apache--2.0-8957e5?style=flat-square)](#status)

	Part of project `imagine` — Interchained

	</div>

	---

	## The failure that built v10

	v9 was asked for a LEFT JOIN analytical query:

	```sql
	-- "List every customer, their total completed spending, sort by spending desc"
	```

	It returned:

	```sql
	SELECT c.id, c.name, COALESCE(o.total, 0) AS total_spending
	FROM customers c
	LEFT JOIN orders o ON c.id
	-- ...truncated. Dead.
	```

	The correct answer:

	```sql
	SELECT c.id, c.name, COALESCE(SUM(o.total), 0) AS total_spending
	FROM customers c
	LEFT JOIN orders o ON c.id = o.customer_id AND o.status = 'completed'
	GROUP BY c.id, c.name
	ORDER BY total_spending DESC, c.id ASC;
	```

	v9 couldn't do multi-clause analytical queries. v10 can.

	---

	## 🎯 What v10 adds

	Where v9 added write capability (`INSERT`/`UPDATE`/`DELETE`), v10 adds complex analytical queries:

	- LEFT JOIN + GROUP BY + COALESCE — the exact v9 failure mode, now handled
	- HAVING filters — post-aggregation conditions
	- Subquery comparisons — nested analytical logic
	- Complex ORDER BY — multi-column, expression-based sorting
	- Multi-table JOINs — beyond simple two-table patterns

	---

	## 📊 The numbers

	\| Model \| Held-out (telemetry) \| Protocol \| Execute rate \| Invented cols \|
	\|---\|---\|---\|---\|---\|
	\| imagine-v8 \| 50.0% (20/40) \| 100% \| — \| — \|
	\| imagine-v9 \| 90.0% (36/40) \| 100% \| 97.5% \| 0 \|
	\| imagine-v10 \| 100.0% (40/40) \| 100% \| 100% \| 0 \|

	Perfect score. Zero wrong answers. Zero hallucinations. Zero refusals.

	---

	## 🧬 How v10 was built

	Two-stage fine-tuning from `Interchained/imagine-v9`:

	### Stage 1 — Full fine-tune (smoke)

	- Corpus: 3,216 admitted pairs (99.94% gate admit rate)
	- New: Analytical query templates (LEFT JOIN aggregations, HAVING, subqueries)
	- Epochs: 3, LR 1e-5, cosine schedule
	- Final loss: 0.0052 (v9 was 0.0320 — 6x better)
	- Time: 4.1 min on H200, 26k tok/s

	### Stage 2 — LoRA refinement

	- Base: v10-smoke checkpoint
	- Rank: 16, LR 5e-6
	- Epochs: 3
	- Final loss: 0.0244
	- Time: 5.4 min on H200
	- Adapter folded into full checkpoint

	### The corpus

	3,218 candidates forged, 3,216 admitted:

	\| Schema \| Templates \| Writes \| Total \|
	\|---\|---\|---\|---\|
	\| shop \| 356 \| 388 \| 744 \|
	\| clinic \| 545 \| 290 \| 835 \|
	\| library \| 481 \| 257 \| 738 \|
	\| fleet \| 297 \| 226 \| 523 \|
	\| audit \| 252 \| 126 \| 378 \|

	Nothing enters training that a live database hasn't agreed with.

	---

	## 🔬 The execution gate

	Every candidate goes through L0–L4:

	```
	candidate SQL ──► L0 real PostgreSQL parser not a regex
	L1 read-only + bounded no writes, no sleeps
	L2 EXPLAIN on live schema hallucinations die HERE
	L3 execute, timed, capped real rows
	L4 SAME ANSWER as ref? ◄── the one that matters
	▼
	admitted to corpus
	```

	68/68 gate tests passing. The gate is the truth authority — not a bigger model, not vibes.

	---

	## 📦 Output protocol

	SQL wrapped in sentinel blocks:

	```
	<<<SQL>>>
	SELECT c.id, c.name, COALESCE(SUM(o.total), 0) AS total_spending
	FROM customers c
	LEFT JOIN orders o ON c.id = o.customer_id AND o.status = 'completed'
	GROUP BY c.id, c.name
	ORDER BY total_spending DESC, c.id ASC;
	<<<END>>>
	```

	Can also refuse:

	\| block \| meaning \|
	\|---\|---\|
	\| `<<<SQL>>>` \| here is your query \|
	\| `<<<UNANSWERABLE>>>` \| this schema cannot answer that \|
	\| `<<<CLARIFY>>>` \| ambiguous — here's what's missing \|

	> ⚠️ A truncated generation is not an answer. An unterminated block extracts to nothing.

	---

	## 💻 Loading v10

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "Interchained/imagine-v10"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)

	# Ask for an analytical query
	prompt = """Schema:
	customers(id, name)
	orders(id, customer_id, total, status)

	List every customer ID, name, and total completed spending.
	Include customers with no completed orders (show 0).
	Sort by spending descending, then ID ascending."""

	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	with torch.no_grad():
	output = model.generate(inputs["input_ids"], max_new_tokens=256, do_sample=False)

	print(tokenizer.decode(output[0], skip_special_tokens=True))
	```

	---

	## 🎯 Identity

	The deployed model identity is Imagine.

	Built and fine-tuned by Interchained.

	DeepSeek-Coder is part of the upstream lineage (via v8 → v9 → v10), but the deployed identity is Imagine.

	---

	## ⚡ Local-first

	Runs on your hardware. No API key. No metered inference. No cloud dependency.

	---

	## 📐 The rules

	1 · Don't write a verifier — the engine already shipped one.
	Real parser. Real planner. Real rows.

	2 · Assert the property, not a proxy.
	Execution accuracy, not string similarity.

	3 · Schema goes in the prompt, not in the weights.
	The model learns "read the schema you were handed" — not memorise ours.

	---

	## ⚠️ Limitations

	v10 is a research checkpoint. It may still:

	- generate incorrect SQL on novel patterns
	- misunderstand ambiguous requests
	- produce writes with wrong `WHERE` clauses — always review before executing

	Generated SQL should be reviewed before use in production. This applies doubly to writes.

	---

	## 🔒 Security

	Do not rely on model behavior alone for database safety. Production systems should enforce:

	- least-privilege database roles
	- statement timeouts and row limits
	- query validation and schema restrictions
	- application-level authorization
	- audit logging
	- human approval for all write statements

	---

	<div align="center">

	Built by [Interchained](https://Interchained.org) · ownership at every layer, including the model

	`3 > 1` — Mark drives, oracle points, Muse builds

	</div>